The new skill in AI is not prompting, it's context engineering

https://www.philschmid.de/context-engineering

915 514 1 year ago

baxtr2• 1 year ago

Conclusion

Building powerful and reliable AI Agents is becoming less about finding a magic prompt or model updates. It is about the engineering of context and providing the right information and tools, in the right format, at the right time. It’s a cross-functional challenge that involves understanding your business use case, defining your outputs, and structuring all the necessary information so that an LLM can “accomplish the task."

That’s actually also true for humans: the more context (aka right info at the right time) you provide the better for solving tasks.

QuercusMax• 1 year ago

Yeah... I'm always asking my UX and product folks for mocks, requirements, acceptance criteria, sample inputs and outputs, why we care about this feature, etc.

Until we can scan your brain and figure out what you really want, it's going to be necessary to actually describe what you want built, and not just rely on vibes.

lupire• 1 year ago

Not "more" context. "Better" context.

(X-Y problem, for example.)

root_axis2• 1 year ago

I am not a fan of this banal trend of superficially comparing aspects of machine learning to humans. It doesn't provide any insight and is hardly ever accurate.

ModernMech4• 1 year ago

I agree, however I do appreciate comparisons to other human-made systems. For example, "providing the right information and tools, in the right format, at the right time" sounds a lot like a bureaucracy, particularly because "right" is decided for you, it's left undefined, and may change at any time with no warning or recourse.

furyofantares3• 1 year ago

I've seen a lot of cases where, if you look at the context you're giving the model and imagine giving it to a human (just not yourself or your coworker, someone who doesn't already know what you're trying to achieve - think mechanical turk), the human would be unlikely to give the output you want.

Context is often incomplete, unclear, contradictory, or just contains too much distracting information. Those are all things that will cause an LLM to fail that can be fixed by thinking about how an unrelated human would do the job.

stefan_• 1 year ago

Theres all these philosophers popping up everywhere. This is also another one of these topics that featured in peoples favorite scifi hyperfixation so all discussions inevitably get ruined with scifi fanfic (see also: room temperature superconductivity).

baxtr2• 1 year ago

Without my note I wouldn’t have seen this comment, which is very insightful to me at least.

https://news.ycombinator.com/item?id=44429880

mentalgear2• 1 year ago

Basically, finding the right buttons to push within the constraints of the environment. Not so much different from what (SW) engineering is, only non-deterministic in the outcomes.

layer8• 1 year ago

The difference is that humans can actively seek to acquire the necessary context by themselves. They don't have to passively sit there and wait for someone else to do the tedious work of feeding them all necessary context upfront. And we value humans who are able to proactively do that seeking by themselves, until they are satisfied that they can do a good job.

simonw42• 1 year ago

The difference is that humans can actively seek to acquire the necessary context by themselves

These days, so can LLM systems. The tool calling pattern got really good in the last six months, and one of the most common uses of that is to let LLMs search for information they need to add to their context.

o3 and o4-mini and Claude 4 all do this with web search in their user-facing apps and it's extremely effective.

The same patterns is increasingly showing up in coding agents, giving them the ability to search for relevant files or even pull in official document documentation for libraries.

fergal• 1 year ago

THis.. I was about to make a similar point; this conclusion reads like a job description for a technical lead role where they managed and define work for a team of human devs who execute implementation.

therealdrag0• 1 year ago

Ya reminds me of social engineering. Like we’re seeing “How to Win Programming and Influence LLMs”.

eviks• 1 year ago

Right info at the right time is not "more", and with humans it's pretty easy to overwhelm, so do the opposite - convert "more" into "wrong"

Davidzheng10• 1 year ago

I think too much context is harmful

simonw42• 1 year ago

I wrote a bit about this the other day: https://simonwillison.net/2025/Jun/27/context-engineering/

Drew Breunig has been doing some fantastic writing on this subject - coincidentally at the same time as the "context engineering" buzzword appeared but actually unrelated to that meme.

How Long Contexts Fail - https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-ho... - talks about the various ways in which longer contexts can start causing problems (also known as "context rot")

How to Fix Your Context - https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.... - gives names to a bunch of techniques for working around these problems including Tool Loadout, Context Quarantine, Context Pruning, Context Summarization, and Context Offloading.

the_mitsuhiko• 1 year ago

Drew Breunig's posts are a must read on this. This is not only important for writing your own agents, it is also critical when using agentic coding right now. These limitations/behaviors will be with us for a while.

outofpaper• 1 year ago

They might be good reads on the topic but Drew makes some significant etymological mistakes. For example loadout doesn't come from gaming but military terminology. It's essentially the same as kit or gear.

storus4• 1 year ago

Those issues are considered artifacts of the current crop of LLMs in academic circles; there is already research allowing LLMs to use millions of different tools at the same time, and stable long contexts, likely reducing the amount of agents to one for most use cases outside interfacing different providers.

Anyone basing their future agentic systems on current LLMs would likely face LangChain fate - built for GPT-3, made obsolete by GPT-3.5.

simonw42• 1 year ago

Can you link to the research on millions of different terms and stable long contexts? I haven't come across that yet.

Foreignborn• 1 year ago

yes, but those aren’t released and even then you’ll always need glue code.

you just need to knowingly resource what glue code is needed, and build it in a way it can scale with whatever new limits that upgraded models give you.

i can’t imagine a world where people aren’t building products that try to overcome the limitations of SOTA models

ZYbCRq22HbJ2y73• 1 year ago

How would "a million different tool calls at the same time" work? For instance, MCP is HTTP based, even at low latency in incredibly parallel environments that would take forever.

dinvlad3• 1 year ago

already research allowing LLMs to use millions of different tools

Hmm first time hearing about this, could you share any examples please?

old_man_cato• 1 year ago

[flagged]

d0gsg0w00f2• 1 year ago

This hits too close to home.

_carbyau_• 1 year ago

[flagged]

jknoepfler• 1 year ago

Oh, and don't forget to retain the artist to correct the ever-increasingly weird and expensive mistakes made by the context when you need to draw newer, fancier pelicans. Maybe we can just train product to draw?

JoeOfTexas• 1 year ago

So who will develop the first Logic Core that automates the context engineer.

igravious• 1 year ago

The first rule of automation: that which can be automated will be automated.

Observation: this isn't anything that can't be automated /

risyachka• 1 year ago

“A month-long skill” after which it won’t be a thing anymore, like so many other.

simonw42• 1 year ago

Most of the LLM prompting skills I figured out ~three years ago are still useful to me today. Even the ones that I've dropped are useful because I know that things that used to be helpful aren't helpful any more, which helps me build an intuition for how the models have improved over time.

orbital-decay• 1 year ago

Many people figured it out two-three years ago when AI-assisted coding basically wasn't a thing, and it's still relevant and will stay relevant. These are fundamental principles, all big models work similarly, not just transformers and not just LLMs.

However, many fundamental phenomena are missing from the "context engineering" scope, so neither context engineering nor prompt engineering are useful terms.

coldtea3• 1 year ago

What exactly month-long AI skills of 2023 AI are obsolete now?

Surely not prompt engineering itself, for example.

tptacek5• 1 year ago

If you're not writing your own agents, you can skip this skill.

dosnem• 1 year ago

Providing context makes sense to me, but do you have any examples of providing context and then getting the AI to produce something complex? I am quite a proponent of AI but even I find myself failing to produce significant results on complex problems, even when I have clone + memory bank, etc. it ends up being a time sink of trying to get the ai to do something only to have me eventually take over and do it myself.

simonw42• 1 year ago

Quite a few times, I've been able to give it enough context to write me an entire working piece of software in a single shot. I use that for plugins pretty often, eg this:

  llm -m openai/o3 \
    -f https://raw.githubusercontent.com/simonw/llm-hacker-news/refs/heads/main/llm_hacker_news.py \
    -f https://raw.githubusercontent.com/simonw/tools/refs/heads/main/github-issue-to-markdown.html \
    -s 'Write a new fragments plugin in Python that registers issue:org/repo/123 which fetches that issue
      number from the specified github repo and uses the same markdown logic as the HTML page to turn that into a fragment'

Which produced this: https://gist.github.com/simonw/249e16edffe6350f7265012bee9e3...

AnotherGoodName• 1 year ago

I had a series of “Using Manim create an animation for formula X rearranging into formula Y with a graph of values of the function”

Beautiful one shot results and i now have really nice animations of some complex maths to help others understand. (I’ll put it up on youtube soon).

I don't know the manim library at all so saved me about a week of work learning and implementing

TZubiri• 1 year ago

Rediscovering encapsulation

Daub3• 1 year ago

For visual art I feel that the existing approaches in context engineering are very much lacking. An Ai understands well enough such simple things as content (bird, dog, owl etc), color (blue green etc) and has a fair understanding of foreground/background. However, the really important stuff is not addressed.

For example: in form, things like negative shape and overlap. In color contrast things like Ratio contrast and dynamic range contrast. Or how manipulating neighboring regional contrast produces tone wrap. I could go on.

One reason for this state of affairs is that artists and designers lack the consistent terminology to describe what they are doing (though this does not stop them from operating at a high level). Indeed, many of the terms I have used here we (my colleagues and I) had to invent ourselves. I would love to work with an AI guru to address this developing problem.

skydhash6• 1 year ago

artists and designers lack the consistent terminology to describe what they are doing

I don't think they do. It may not be completely consistent, but open any art book and you find the same thing being explained again and again. Just for drawing humans, you will find emphasis on the skeleton and muscle volume for forms and poses, planes (especially the head) for values and shadows, some abstract things like stability and line weight, and some more concrete things like foreshortening.

Several books and course have gone over those concepts. They are not difficult to explain, they are just difficult to master. That's because you have to apply judgement for every single line or brush stroke deciding which factors matter most and if you even want to do the stroke. Then there's the whole hand eye coordination.

So unless you can solve judgement (which styles derive from), there's not a lot of hope there.

ADDENDUM

And when you do a study of another's work, it's not copying the data, extracting colors, or comparing labels,... It's just studying judgement. You know the complete formula from which a more basic version is being used for the work, and you only want to know the parameters. Whereas machine training is mostly going for the wrong formula with completely different variables.

arbitrary_name• 1 year ago

From the first link:Read large enough context to ensure you get what you need.

How does this actually work, and how can one better define this to further improve the prompt?

This statement feels like the 'draw the rest of the fucking owl' referred to elsewhere in the thread

simonw42• 1 year ago

I'm not sure how you ended up on that page... my comment above links to https://simonwillison.net/2025/Jun/27/context-engineering/

The "Read large enough context to ensure you get what you need" quote is from a different post entirely, this one: https://simonwillison.net/2025/Jun/30/vscode-copilot-chat/

That's part of the system prompts used by the GitHub Copilot Chat extension for VS Code - from this line: https://github.com/microsoft/vscode-copilot-chat/blob/40d039...

The full line is:

  When using the {ToolName.ReadFile} tool, prefer reading a
  large section over calling the {ToolName.ReadFile} tool many
  times in sequence. You can also think of all the pieces you
  may be interested in and read them in parallel. Read large
  enough context to ensure you get what you need.

That's a hint to the tool-calling LLM that it should attempt to guess which area of the file is most likely to include the code that it needs to review.

It makes more sense if you look at the definition of the ReadFile tool:

https://github.com/microsoft/vscode-copilot-chat/blob/40d039...

  description: 'Read the contents of a file. Line numbers are
  1-indexed. This tool will truncate its output at 2000 lines
  and may be called repeatedly with offset and limit parameters
  to read larger files in chunks.'

The tool takes three arguments: filePath, offset and limit.

daxfohl4• 1 year ago

I'm surprised there isn't already an ecosystem of libraries that just do this. When building agents you either have to roll your own or copy an algorithm out of some article.

I'd expect this to be a lot more plug and play, and as swappable as LLMs themselves by EOY, along with a bunch of tooling to help with observability, A/B testing, cost and latency analysis (since changing context kills the LLM cache), etc.

daxfohl4• 1 year ago

Or maybe it's that each of these things is pretty simple in itself. Clipping context is one line of code, summarizing could be a couple lines to have an LLM summarize it for you, etc. So not substantial enough for a formal library. Whereas the combinations of these techniques is very application dependent, so not reusable enough to warrant separating as an independent library.

Or maybe it just hasn't matured yet and we'll see more of it in the future. We'll see.

crystal_revenge• 1 year ago

Definitely mirrors my experience. One heuristic I've often used when providing context to model is "is this enough information for a human to solve this task?". Building some text2SQL products in the past it was very interesting to see how often when the model failed, a real data analyst would reply something like "oh yea, that's an older table we don't use any more, the correct table is...". This means the model was likely making a mistake that a real human analyst would have without the proper context.

One thing that is missing from this list is: evaluations!

I'm shocked how often I still see large AI projects being run without any regard to evals. Evals are more important for AI projects than test suites are for traditional engineering ones. You don't even need a big eval set, just one that covers your problem surface reasonably well. However without it you're basically just "guessing" rather than iterating on your problem, and you're not even guessing in a way where each guess is an improvement on the last.

edit: To clarify, I ask myself this question. It's frequently the case that we expect LLMs to solve problems without the necessary information for a human to solve them.

kevin_thibedeau• 1 year ago

Asking yes no questions will get you a lie 50% of the time.

adriand• 1 year ago

I have pretty good success with asking the model this question before it starts working as well. I’ll tell it to ask questions about anything it’s unsure of and to ask for examples of code patterns that are in use in the application already that it can use as a template.

hobs2• 1 year ago

The thing is, all the people cosplaying as data scientists don't want evaluations, and that's why you saw so little in fake C level projects, because telling people the emperor has no clothes doesn't pay.

For those actually using the products to make money well, hey - all of those have evaluations.

shermantanktop2• 1 year ago

I know this proliferation of excited wannabes is just another mark of a hype cycle, and there’s real value this time. But I find myself unreasonably annoyed by people getting high on their own supply and shouting into a megaphone.

adiabatichottub• 1 year ago

A classic law of computer programming:

"Make it possible for programmers to write in English and you will find that programmers cannot write in English."

It's meant to be a bit tongue-in-cheek, but there is a certain truth to it. Most human languages fail at being precise in their expression and interpretation. If you can exactly define what you want in English, you probably could have saved yourself the time and written it in a machine-interpretable language.

bGl2YW5j4• 1 year ago

Saw this the other day and it made me think that too much effort and credence is being given to this idea of crafting the perfect environment for LLMs to thrive in. Which to me, is contrary to how powerful AI systems should function. We shouldn’t need to hold its hand so much.

Obviously we’ve got to tame the version of LLMs we’ve got now, and this kind of thinking is a step in the right direction. What I take issue with is the way this thinking is couched as a revolutionary silver bullet.

gametorch• 1 year ago

It's still way easier for me to say

"here's where to find the information to solve the task"

than for me to manually type out the code, 99% of the time

ramesh31• 1 year ago

We shouldn't but it's analogous to how CPU usage used to work. In the 8 bit days you could do some magical stuff that was completely impossible before microcomputers existed. But you had to have all kinds of tricks and heuristics to work around the limited abilities. We're in the same place with LLMs now. Some day we will have the equivalent of what gigabytes or RAM are to a modern CPU now, but we're still stuck in the 80s for now (which was revolutionary at the time).

smeej• 1 year ago

It also reminds me of when you could structure an internet search query and find exactly what you wanted. You just had to ask it in the machine's language.

I hope the generalized future of this doesn't look like the generalized future of that, though. Now it's darn near impossible to find very specific things on the internet because the search engines will ignore any "operators" you try to use if they generate "too few" results (by which they seem to mean "few enough that no one will pay for us to show you an ad for this search"). I'm moderately afraid the ability to get useful results out of AIs will be abstracted away to some lowest common denominator of spammy garbage people want to "consume" instead of use for something.

bGl2YW5j4• 1 year ago

Good points that you and Aleksiy have made. Thanks for enhancing my perspective!

4ndrewl• 1 year ago

Reminds me of first gen chatbots where the user had to put in the effort of trying to craft a phrase in a way that would garner the expected result. It's a form of user-hostility.

aleksiy1232• 1 year ago

It may not be a silver bullet, in that it needs lots of low level human guidance to do some complex task.

But looking at the trend of these tools, the help they are requiring is become more and more higher level, and they are becoming more and more capable of doing longer more complex tasks as well as being able to find the information they need from other systems/tools (search, internet, docs, code etc...).

I think its that trend that really is the exciting part, not just its current capabilities.

asadotzler2• 1 year ago

why is it that so many of you think there's anything meaningfully predictable based on these past trends? what on earth makes you belive the line keeps going as it has, when there's literally nothing to base that belief on. it's all just wishful thinking.

pwarner• 1 year ago

It's an integration adventure. This is why much AI is failing in the enterprise. MS Copilot is moderately interesting for data in MS Office, but forget about it accessing 90% of your data that's in other systems.

JohnMakin• 1 year ago

Building powerful and reliable AI Agents is becoming less about finding a magic prompt or model updates.

Ok, I can buy this

It is about the engineering of context and providing the right information and tools, in the right format, at the right time.

when the "right" format and "right" time are essentially, and maybe even necessarily, undefined, then aren't you still reaching for a "magic" solution?

If the definition of "right" information is "information which results in a sufficiently accurate answer from a language model" then I fail to see how you are doing anything fundamentally differently than prompt engineering. Since these are non-deterministic machines, I fail to see any reliable heuristic that is fundamentally indistinguishable than "trying and seeing" with prompts.

edwardbernays• 1 year ago

The state of the art theoretical frameworks typically separates these into two distinct exploratory and discovery phases. The first phase, which is exploratory, is best conceptualized as utilizing an atmospheric dispersion device. An easily identifiable marker material, usually a variety of feces, is metaphorically introduced at high velocity. The discovery phase is then conceptualized as analyzing the dispersal patterns of the exploratory phase. These two phases are best summarized, respectively, as "Fuck Around" followed by "Find Out."

mentalgear2• 1 year ago

It's magical thinking all the way down. Whether they call it now "prompt" or "context" engineering because it's the same tinkering to find something that "sticks" in non-deterministic space.

nonethewiser• 1 year ago

Whether they call it now "prompt" or "context" engineering because it's the same tinkering to find something that "sticks" in non-deterministic space.

I dont quite follow. Prompts and contexts are different things. Sure, you can get thing into contexts with prompts but that doesn't mean they are entirely the same.

You could have a long running conversation with a lot in the context. A given prompt may work poorly, whereas it would have worked quite well earlier. I don't think this difference is purely semantic.

For whatever it's worth I've never liked the term "prompt engineering." It is perhaps the quintessential example of overusing the word engineering.

surecoocoocoo• 1 year ago

We used to define a specification.

In other words; context.

But that was like old man programming.

As the laws of physics changed between 1970 and 2009.

ironmagma• 1 year ago

What is all software but tinkering?

I mean this not as an insult to software dev but to work generally. It’s all play in the end.

belter• 1 year ago

Got it...updating CV to call myself a VibeOps Engineer in a team of Context Engineers...A few of us were let go last quarter, as they could only do Prompt Engineering.

tootie2• 1 year ago

You say "magic" I say "heuristic"

dinvlad3• 1 year ago

when the "right" format and "right" time are essentially, and maybe even necessarily, undefined, then aren't you still reaching for a "magic" solution?

Exactly the problem with all "knowing how to use AI correctly" advice out there rn. Shamans with drums, at the end of the day :-)

andy99• 1 year ago

It's called over-fitting, that's basically what prompt engineering is.

evjan• 1 year ago

That doesn't sound like how I understand over-fitting, but I'm intrigued! How do you mean?

FridgeSeal• 1 year ago

It’s just AI people moving the goalposts now that everyone has realised that “prompt engineering” isn’t a special skill.

coliveira4• 1 year ago

In other words, "if AI doesn't work for you the problem is not IA, it is the user", that's what AI companies want us to believe.

j45• 1 year ago

Everything is new to someone and the tends of reference will evolve.

csallen• 1 year ago

This is like telling a soccer player that no change in practice or technique is fundamentally different than another, because ultimately people are non-deterministic machines.

felipeerias• 1 year ago

If someone asked you about the usages of a particular element in a codebase, you would probably give a more accurate answer if you were able to use a code search tool rather than reading every source file from top to bottom.

For that kind of tasks (and there are many of those!), I don't see why you would expect something fundamentally different in the case of LLMs.

skydhash6• 1 year ago

But why not provide the search tool instead of being an imperfect interface between it and the person asking? The only reason for the latter is that you have more applied knowledge in the context and can use the tool better. For any other case, the answer should be “use this tool”.

manishsharan• 1 year ago

I provided 'grep' as a tool to LLM (deepseek) and it does a better job of finding usages. This is especially true if the code is obfuscated JavaScript.

bostik• 1 year ago

In my previous job I repeatedly told people that "git grep is a superpower". Especially in a monorepo, but works well in any big repository, really.

To this day I think the same. With the addition that knowing about "git log -S" grants you necromancy in addition to the regular superpowers. Ability to do rapid code search, and especially code history search, make you look like a wizard without the funny hat.

Aeolun2• 1 year ago

There is only so much you can do with prompts. To go from the 70% accuracy you can achieve with that to the 95% accuracy I see in Claude Code, the context is absolutely the most important, and it’s visible how much effort goes into making sure Claude retrieves exactly the right context, often at the expense of speed.

majormajor2• 1 year ago

Why are we drawing a difference between "prompt" and "context" exactly? The linked article is a bit of puffery that redefines a commonly-used term - "context" - to mean something different than what it's meant so far when we discuss "context windows." It seems to just be some puffery to generate new hype.

When you play with the APIs the prompt/context all blurs together into just stuff that goes into the text fed to the model to produce text. Like when you build your own basic chatbot UI and realize you're sending the whole transcript along with every step. Using the terms from the article, that's "State/History." Then "RAG" and "Long term memory" are ways of working around the limits of context window size and the tendency of models to lose the plot after a huge number of tokens, to help make more effective prompts. "Available tools" info also falls squarely in the "prompt engineering" category.

The reason prompt engineering is going the way of the dodo is because tools are doing more of the drudgery to make a good prompt themselves. E.g., finding relevant parts of a codebase. They do this with a combination of chaining multiple calls to a model together to progressively build up a "final" prompt plus various other less-LLM-native approaches (like plain old "find").

So yeah, if you want to build a useful LLM-based tool for users you have to write software to generate good prompts. But... it ain't really different than prompt engineering other than reducing the end user's need to do it manually.

It's less that we've made the AI better and more that we've made better user interfaces than just-plain-chat. A chat interface on a tool that can read your code can do more, more quickly, than one that relies on you selecting all the relevant snippets. A visual diff inside of a code editor is easier to read than a markdown-based rendering of the same in a chat transcript. Etc.

ninetyninenine2• 1 year ago

Yeah but do we have to make a new buzz word out of it? "Context engineer"

autobodie• 1 year ago

Tha problem is that "right" is defined circularly

PeterStuer2• 1 year ago

"these are non-deterministic machines"

Only if you choose so by allowing some degree of randomness with the temperature setting.

zelphirkalt2• 1 year ago

This is what irks me so often when reading these comments. This is just software inside a ordinary computer, it always does the same with the same input, which includes hidden and global state. Stating that they are "non-deterministic machines" sounds like throwing the towel and thinking "it's magic!". I am not even sure what people want to actually express, when they make these false statements.

If one wants to make something give the same answers every time, one needs to control all the variables of input. This is like any other software including other machine learning algorithms.

edflsafoiewq• 1 year ago

In the strict sense, sure, but the point is they depend not only on the seed but on seemingly minor variations in the prompt.

pegasus• 1 year ago

They are usually nondeterministic even at temperature 0 - due to things like parallelism and floating point rounding errors.

pbreit• 1 year ago

What's the difference?

colordrops• 1 year ago

Since these are non-deterministic machines, I fail to see any reliable heuristic that is fundamentally indistinguishable than "trying and seeing" with prompts

There are many sciences involving non-determinism that still have laws and patterns, e.g. biology and maybe psychology. It's not all or nothing.

Also, LLMs are deterministic, just not predictable. The non-determinism is injected by providers.

Anyway is there an essential difference between prompt engineering and context engineering? They seem like two names for the same thing.

simonw42• 1 year ago

They arguably are two names for the same thing.

The difference is that "prompt engineering" as a term has failed, because to a lot of people the inferred definition is "a laughably pretentious term for typing text into a chatbot" - it's become indistinguishable from end-user prompting.

My hope is that "context engineering" better captures the subtle art of building applications on top of LLMs through carefully engineering their context.

v3ss0n4• 1 year ago

At this point , due to non-deterministic nature and hallucination context engineering is pretty much magic. But here are our findings.

1 - LLM Tends to pick up and understand contexts that comes at top 7-12 lines.Mostly first 1k token is best understood by llms ( tested on Claude and several opensource models ) so - most important contexts like parsing rules need to be placed there.

2 - Need to keep context short . Whatever context limit they claim is not true . They may have long context window of 1 mil tokens but only up to avg 10k token have good accuracy and recall capabilities , the rest is just bunk , just ignore them. Write the prompt and try compressing/summerizing it without losing key information manually or use of LLM.

3 - If you build agent-to-agent orchestration , don't build agents with long context and multiple tools, break them down to several agents with different set of tools and then put a planning agent which solely does handover.

4 - If all else fails , write agent handover logic in code - as it always should.

From building 5+ agent to agent orchestration project on different industries using autogen + Claude - that is the result.

lblume• 1 year ago

I have uploaded entire books to the latest Gemini and had the model reliably accurately answer specific questions requiring knowledge of multiple chapters.

zacksiri• 1 year ago

Based on my testing the larger the model the better it is at handling larger context.

I tested with 8B model, 14B model and 32B model.

I wanted it to create structured json, and the context was quite large like 60k tokens.

the 8B model failed miserably despite supporting 128k context, the 14b did better the 32B one almost got everything correct. However when jumping to a really large model like grok-3-mini it got it all perfect.

The 8B, 14B, 32B models I tried were Qwen 3. All the models I tested I disabled thinking.

Now for my agent workflows I use small models for most workflow (it works quite nicely) and only use larger models when the problem is harder.

zvitiate• 1 year ago

Claude’s system prompt is SO long though that the first 1k lines might not be as relevant for Gemini, GPT, or Grok.

phyalow2• 1 year ago

“non-deterministic machines“

Not correct. They are deterministic as long as a static seed is used.

kazga• 1 year ago

That's not true in practice. Floating point arithmetic is not commutative due to rounding errors, and the parallel operations introduce non-determinisn even at temperature 0.

thomastjeffery• 1 year ago

Models are Biases.

There is no objective truth. Everything is arbitrary.

There is no such thing as "accurate" or "precise". Instead, we get to work with "consistent" and "exhaustive". Instead of "calculated", we get "decided". Instead of "defined" we get "inferred".

Really, the whole narrative about "AI" needs to be rewritten from scratch. The current canonical narrative is so backwards that it's nearly impossible to have a productive conversation about it.

ModernMech4• 1 year ago

"Wow, AI will replace programming languages by allowing us to code in natural language!"

"Actually, you need to engineer the prompt to be very precise about what you want to AI to do."

"Actually, you also need to add in a bunch of "context" so it can disambiguate your intent."

"Actually English isn't a good way to express intent and requirements, so we have introduced protocols to structure your prompt, and various keywords to bring attention to specific phrases."

"Actually, these meta languages could use some more features and syntax so that we can better express intent and requirements without ambiguity."

"Actually... wait we just reinvented the idea of a programming language."

throwawayoldie2• 1 year ago

Only without all that pesky determinism and reproducibility.

(Whoever's about to say "well ackshually temperature of zero", don't.)

whatevertrevor• 1 year ago

You forgot about lower performance and efficiency. And longer build/run cycles. And more hardware/power usage.

mindok• 1 year ago

“Actually - curly braces help save space in the context while making meaning clearer”

georgeburdell• 1 year ago

We should have known up through Step 4 for a while. See: the legal system

nimish• 1 year ago

A half baked programming language that isn't deterministic or reproducible or guaranteed to do what you want. Worst of all worlds unless your input and output domains are tolerant to that, which most aren't. But if they are, then it's great

eddythompson802• 1 year ago

Which is funny because everyone is already looking at AI as: I have 30 TB of shit that is basically "my company". Can I dump that into your AI and have another, magical, all-konwning, co-worker?

coliveira4• 1 year ago

Which I think it is double funny because, given the zeal with which companies are jumping into this bandwagon, AI will bankrupt most businesses in record time! Just imagine the typical company firing most workers and paying a fortune to run on top of a schizophrenic AI system that gets things wrong half of the time...

eddythompson802• 1 year ago

Yes, you can see the insanely accelerated pace of bankruptcies or "strategic realignments" among AI startups.

I think it's just game theory in play and we can do nothing but watch it play out. The "up side" is insane, potentially unlimited. The price is high, but so is the potential reward. By the rules of the game, you have to play. There is no other move you can make. No one knows the odds, but we know the potential reward. You could be the next T company easy. You could realistically go from startup -> 1 Trillion in less than a year if you are right.

We need to give this time to play itself out. The "odds" will eventually be better estimated and it'll affect investment. In the mean time, just give your VC Google's, Microsoft's, or AWS's direct deposit info. It's easier that way.

whimsicalism• 1 year ago

i think context engineering as described is somewhat a subset of ‘environment engineering.’ the gold-standard is when an outcome reached with tools can be verified as correct and hillclimbed with RL. most of the engineering effort is from building the environment and verifier while the nuts and bolts of grpo/ppo training and open-weight tool-using models are commodities.

intellectronica• 1 year ago

See also: https://ai.intellectronica.net/context-engineering for an overview.

jshorty• 1 year ago

I have felt somewhat frustrated with what I perceive as a broad tendency to malign "prompt engineering" as an antiquated approach for whatever new the industry technique is with regards to building a request body for a model API. Whether that's RAG years ago, nuance in a model request's schema beyond simple text (tool calls, structured outputs, etc), or concepts of agentic knowledge and memory more recently.

While models were less powerful a couple of years ago, there was nothing stopping you at that time from taking a highly dynamic approach to what you asked of them as a "prompt engineer"; you were just more vulnerable to indeterminism in the contract with the models at each step.

Context windows have grown larger; you can fit more in now, push out the need for fine-tuning, and get more ambitious with what you dump in to help guide the LLM. But I'm not immediately sure what skill requirements fundamentally change here. You just have more resources at your disposal, and can care less about counting tokens.

simonw42• 1 year ago

I liked what Andrej Karpathy had to say about this:

https://twitter.com/karpathy/status/1937902205765607626

[..] in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step. Science because doing this right involves task descriptions and explanations, few shot examples, RAG, related (possibly multimodal) data, tools, state and history, compacting... Too little or of the wrong form and the LLM doesn't have the right context for optimal performance. Too much or too irrelevant and the LLM costs might go up and performance might come down. Doing this well is highly non-trivial. And art because of the guiding intuition around LLM psychology of people spirits.

bgwalter2• 1 year ago

All that work just for stripping a license. If one uses code directly from GitHub, copy and paste is sufficient. One can even keep the license.

saejox• 1 year ago

Claude 3.5 was released 1 year ago. Current LLMs are not much better at coding than it. Sure they are more shiny and well polished, but not much better at all. I think it is time to curb our enthusiasm.

I almost always rewrite AI written functions in my code a few weeks later. Doesn't matter they have more context or better context, they still fail to write code easily understandable by humans.

simonw42• 1 year ago

Claude 3.5 was remarkably good at writing code. If Claude 3.7 and Claude 4 are just incremental improvements on that then even better!

I actually think they're a lot more than incremental. 3.7 introduced "thinking" mode and 4 doubled down on that and thinking/reasoning/whatever-you-want-to-call-it is particularly good at code challenges.

As always, if you're not getting great results out of coding LLMs it's likely you haven't spent several months iterating on your prompting techniques to figure out what works best for your style of development.

davidclark2• 1 year ago

Good example of why I have been totally ignoring people who beat the drum of needing to develop the skills of interacting with models. “Learn to prompt” is already dead? Of course, the true believers will just call this an evolution of prompting or some such goalpost moving.

Personally, my goalpost still hasn’t moved: I’ll invest in using AI when we are past this grand debate about its usefulness. The utility of a calculator is self-evident. The utility of an LLM requires 30k words of explanation and nuanced caveats. I just can’t even be bothered to read the sales pitch anymore.

simonw42• 1 year ago

We should be so far past the "grand debate about its usefulness" at this point.

If you think that's still a debate, you might be listening to the small pool of very loud people who insist nothing has improved since the release of GPT-4.

fragmede• 1 year ago

Should be, but the bar for scientifically proven is high. Absent actual studies showing this, (and with a large N), people will refuse to believe things they don't want to be true.

nandhinianand• 1 year ago

I think this is definitely true for novel writing and stuff like that based on my experiments with AI so far.. I'm still on the fence about coding/building s/w based on it, but that may just be about the unlearning and re-learning i'm yet to do/try out.

davidclark2• 1 year ago

Have you considered the opposite? Reflected on your own biases?

I’m listening to my own experience. Just today I gave it another fair shot. GitHub Copilot agent mode with GPT-4.1. Still unimpressed.

This is a really insightful look at why people perceive the usefulness of these models differently. It is fair to both sides without being dismissive as one side just not “getting it” or how we should be “so far” past debate:

https://ferd.ca/the-gap-through-which-we-praise-the-machine....

_pdp_• 1 year ago

It is wrong. The new/old skill is reverse engineering.

If the majority of the code is generated by AI, you'll still need people with technical expertise to make sense of it.

CamperBob27• 1 year ago

Not really. Got some code you don't understand? Feed it to a model and ask it to add comments.

Ultimately humans will never need to look at most AI-generated code, any more than we have to look at the machine language emitted by a C compiler. We're a long way from that state of affairs -- as anyone who struggled with code-generation bugs in the first few generations of compilers will agree -- but we'll get there.

rvz5• 1 year ago

Not really. Got some code you don't understand? Feed it to a model and ask it to add comments.

Absolutely not.

An experienced individual in their field can tell if the AI made a mistake in the comments / code rather than the typical untrained eye.

So no, actually read the code and understand what it does.

Ultimately humans will never need to look at most AI-generated code, any more than we have to look at the machine language emitted by a C compiler.

So for safety critical systems, one should not look or check if code has been AI generated?

inspectorwadget2• 1 year ago

any more than we have to look at the machine language emitted by a C compiler.

Some developers do actually look at the output of C compilers, and some of them even spend a lot of time criticizing that output by a specific compiler (even writing long blog posts about it). The C language has an ISO specification, and if a compiler does not conform to that specification, it is considered a bug in that compiler.

You can even go to godbolt.org / compilerexplorer.org and see the output generated for different targets by different compilers for different languages. It is a popular tool, also for language development.

I do not know what prompt engineering will look like in the future, but without AGI, I remain skeptical about verification of different kinds of code not being required in at least a sizable proportion of cases. That does not exclude usefulness of course: for instance, if you have a case where verification is not needed; or verification in a specific case can be done efficiently and robustly by a relevant expert; or some smart method for verification in some cases, like a case where a few primitive tests are sufficient.

But I have no experience with LLMs or prompt engineering.

I do, however, sympathize with not wanting to deal with paying programmers. Most are likely nice, but for instance a few may be costly, or less than honest, or less than competent, etc. But while I think it is fine to explore LLMs and invest a lot into seeing what might come of them, I would not personally bet everything on them, neither in the short term nor the long term.

May I ask what your professional background and experience is?

fumblingness• 1 year ago

"And at no point does it ever occur to you to demand proof that measures such as this will have the desired effect... or, indeed, that the desired effect is indeed worth achieving at all."

- you (https://news.ycombinator.com/item?id=44439447)

adhamsalama• 1 year ago

There is no engineering involved in using AI. It's insulting to call begging an LLM "engineering".

rednafi2• 1 year ago

This. Convincing a bullshit generator to give you the right data isn’t engineering, it quackery. But I guess “context quackery” wouldn’t sell as much.

LLMs are quite useful and I leverage them all the time. But I can’t stand these AI yappers saying the same shit over and over again in every media format and trying to sell AI usage as some kind of profound wizardry when it’s not.

mikhmha• 1 year ago

It is total quackery. When you zoom out in these discussions you begin to see how the AI yappers and their methodology is just modern-day alchemy with its own jargon and "esoteric" techniques.

Zopieux5• 1 year ago

That's the definition of a hype cycle. Can't wait for tech to be past it.

8organicbits• 1 year ago

One thought experiment I was musing on recently was the minimal context required to define a task (to an LLM, human, or otherwise). In software, there's a whole discipline of human centered design that aims to uncover the nuance of a task. I've worked with some great designers, and they are incredibly valuable to software development. They develop journey maps, user stories, collect requirements, and produce a wealth of design docs. I don't think you can successfully build large projects without that context.

I've seen lots of AI demos that prompt "build me a TODO app", pretend that is sufficient context, and then claim that the output matches their needs. Without proper context, you can't tell if the output is correct.

grafmax2• 1 year ago

There is no need to develop this ‘skill’. This can all be automated as a preprocessing step before the main request runs. Then you can have agents with infinite context, etc.

simonw42• 1 year ago

You need this skill if you're the engineer that's designing and implementing that preprocessing step.

yunwal• 1 year ago

Non-rhetorical question: is this different enough from data engineering that it needs it’s own name?

ofjcihen• 1 year ago

Not at all, just ask the LLM to design and implement it.

AI turtles all the way down.

dolebirchwood• 1 year ago

The skill amounts to determining "what information is required for System A to achieve Outcome X." We already have a term for this: Critical thinking.

grafmax2• 1 year ago

In the short term horizon I think you are right. But over a longer horizon, we should expect model providers to internalize these mechanisms, similar to how chain of thought has been effectively “internalized” - which in turn has reduced the effectiveness that prompt engineering used to provide as models have gotten better.

lawlessone• 1 year ago

I look forward to 5 million LinkedIn posts repeating this

pyman3• 1 year ago

Someone needs to build a Chrome extension called "BS Analysis" for LinkedIn

octo888• 1 year ago

"The other day my colleague walked up to me and said Jon, prompting is the new skill that's needed.

I laughed and told them there wrong. Here's why ->"

labrador2• 1 year ago

I’m curious how this applies to systems like ChatGPT, which now have two kinds of memory: user-configurable memory (a list of facts or preferences) and an opaque chat history memory. If context is the core unit of interaction, it seems important to give users more control or at least visibility into both.

I know context engineering is critical for agents, but I wonder if it's also useful for shaping personality and improving overall relatability? I'm curious if anyone else has thought about that.

simonw42• 1 year ago

I really dislike the new ChatGPT memory feature (the one that pulls details out of a summarized version of all of your previous chats, as opposed to older memory feature that records short notes to itself) for exactly this reason: it makes it even harder for me to control the context when I'm using ChatGPT.

If I'm debugging something with ChatGPT and I hit an error loop, my fix is to start a new conversation.

Now I can't be sure ChatGPT won't include notes from that previous conversation's context that I was trying to get rid of!

Thankfully you can turn the new memory thing off, but it's on by default.

I wrote more about that here: https://simonwillison.net/2025/May/21/chatgpt-new-memory/

labrador2• 1 year ago

On the other hand, for my use case (I'm retired and enjoy chatting with it), having it remember items from past chats makes it feel much more personable. I actually prefer Claude, but it doesn't have memory, so I unsubscribed and subscribed to ChatGPT. That it remembers obscure but relevant details about our past chats feels almost magical.

It's good that you can turn it off. I can see how it might cause problems when trying to do technical work.

Edit: Note, the introduction of memory was a contributing factor to "the sychophant" that OpenAI had to rollback. When it could praise you while seeming to know you was encouraging addictive use.

Edit2: Here's the previous Hacker News discussion on Simon's "I really don’t like ChatGPT’s new memory dossier"

https://news.ycombinator.com/item?id=44052246

ozim• 1 year ago

Finding a magic prompt was never “prompt engineering” it was always “context engineering” - lots of “AI wannabe gurus” sold it as such but they never knew any better.

RAG wasn’t invented this year.

Proper tooling that wraps esoteric knowledge like using embeddings, vector dba or graph dba becomes more mainstream. Big players improve their tooling so more stuff is available.

semiinfinitely• 1 year ago

context engineering is just a phrase that karpathy uttered for the first time 6 days ago and now everyone is treating it like its a new field of science and engineering

hnthrow90348765• 1 year ago

Cool, but wait another year or two and context engineering will be obsolete as well. It still feels like tinkering with the machine, which is what AI is (supposed to be) moving us away from.

hobs2• 1 year ago

Probably impossible unless computers themselves change in another year or two.

alganet4• 1 year ago

If I need to do all this work (gather data, organize it, prepare it, etc), there are other AI solutions I might decide to use instead of an LLM.

joe51504• 1 year ago

You might as well use your natural intelligence instead of the artificial stuff at that point.

coliveira4• 1 year ago

Yes, when all is said and done people will realize that artificial intelligence is too expensive to replace natural intelligence. AI companies want to avoid this realization for as long as possible.

alganet4• 1 year ago

I'm assuming the post is about automated "context engineering". It's not a human doing it.

In this arrangement, the LLM is a component. What I meant is that it seems to me that other non-LLM AI technologies would be a better fit for this kind of thing. Lighter, easier to change and adapt, potentially even cheaper. Not for all scenarios, but for a lot of them.

simonw42• 1 year ago

What kind of alternative AI solutions might you use here?

alganet4• 1 year ago

Classifiers to classify things, traditional neural nets to identify things. Typical run of the mill.

In OpenAI hype language, this is a problem for "Software 2.0", not "Software 3.0" in 99% of the cases.

The thing about matching an informal tone would be the hard part. I have to concede that LLMs are probably better at that. But I have the feeling that this is not exactly the feature most companies are looking for, and they would be willing to not have it for a cheaper alternative. Most of them just don't know that's possible.

la64710• 1 year ago

Of course the best prompts automatically included providing the best (not necessarily most) context to extract the right output.

m3kw93• 1 year ago

Well, it’s still a prompt

bradhe• 1 year ago

Back in my day we just called this "knowing what to google" but alright, guys.

rednafi2• 1 year ago

I really don’t get this rush to invent neologisms to describe every single behavioral artifact of LLMs. Maybe it’s just a yearning to be known as the father of Deez Unseen Mind-blowing Behaviors (DUMB).

LLM farts — Stochastic Wind Release.

The latest one is yet another attempt to make prompting sound like some kind of profound skill, when it’s really not that different from just knowing how to use search effectively.

Also, “context” is such an overloaded term at this point that you might as well just call it “doing stuff” — and you’d objectively be more descriptive.

jongjong• 1 year ago

Recently I started work on a new project and I 'vibe coded' a test case for a complex OAuth token expiry bug entirely with AI (with Cursor), complete with mocks and stubs... And it was on someone else's project. I had no prior familiarity with the code.

That's when I understood that vibe coding is real and context is the biggest hurdle.

That said, most of the context could not be pulled from the codebase directly but came from me after asking the AI to check/confirm certain things that I suspected could be the problem.

I think vibe coding can be very powerful in the hands of a senior developer because if you're the kind of person who can clearly explain their intuitions with words, it's exactly the missing piece that the AI needs to solve the problem... And you still need to do code review aspect which is also something which senior devs are generally good at. Sometimes it makes mistakes/incorrect assumptions.

I'm feeling positive about LLMs. I was always complaining about other people's ugly code before... I HATE over-modularized, poorly abstracted code where I have to jump across 5+ different files to figure out what a function is doing; with AI, I can just ask it to read all the relevant code across all the files and tell me WTF the spaghetti is doing... Then it generates new code which 'follows' existing 'conventions' (same level of mess). The AI basically automates the most horrible aspect of the work; making sense of the complexity and churning out more complexity that works. I love it.

That said, in the long run, to build sustainable projects, I think it will require following good coding conventions and minimal 'low code' coding... Because the codebase could explode in complexity if not used carefully. Code quality can only drop as the project grows. Poor abstractions tend to stick around and have negative flow-on effects which impact just about everything.

colgandev• 1 year ago

I've been finding a ton of success lately with speech to text as the user prompt, and then using https://continue.dev in VSCode, or Aider, to supply context from files from my projects and having those tools run the inference.

I'm trying to figure out how to build a "Context Management System" (as compared to a Content Management System) for all of my prompts. I completely agree with the premise of this article, if you aren't managing your context, you are losing all of the context you create every time you create a new conversation. I want to collect all of the reusable blocks from every conversation I have, as well as from my research and reading around the internet. Something like a mashup of Obsidian with some custom Python scripts.

The ideal inner loop I'm envisioning is to create a "Project" document that uses Jinja templating to allow transclusion of a bunch of other context objects like code files, documentation, articles, and then also my own other prompt fragments, and then to compose them in a master document that I can "compile" into a "superprompt" that has the precise context that I want for every prompt.

Since with the chat interfaces they are always already just sending the entire previous conversation message history anyway, I don't even really want to use a chat style interface as much as just "one shotting" the next step in development.

It's almost a turn based game: I'll fiddle with the code and the prompts, and then run "end turn" and now it is the llm's turn. On the llm's turn, it compiles the prompt and runs inference and outputs the changes. With Aider it can actually apply those changes itself. I'll then review the code using diffs and make changes and then that's a full turn of the game of AI-assisted code.

I love that I can just brain dump into speech to text, and llms don't really care that much about grammar and syntax. I can curate fragments of documentation and specifications for features, and then just kind of rant and rave about what I want for a while, and then paste that into the chat and with my current LLM of choice being Claude, it seems to work really quite well.

My Django work feels like it's been supercharged with just this workflow, and my context management engine isn't even really that polished.

If you aren't getting high quality output from llms, definitely consider how you are supplying context.

patrickhogan1• 1 year ago

OpenAI’s o3 searches the web behind a curtain: you get a few source links and a fuzzy reasoning trace, but never the full chunk of text it actually pulled in. Without that raw context, it’s impossible to audit what really shaped the answer.

simonw42• 1 year ago

Yeah, I find that really frustrating.

I understand why they do it though: if they presented the actual content that came back from search they would absolutely get in trouble for copyright-infringement.

I suspect that's why so much of the Claude 4 system prompt for their search tool is the message "Always respect copyright by NEVER reproducing large 20+ word chunks of content from search results" repeated half a dozen times: https://simonwillison.net/2025/May/25/claude-4-system-prompt...

Zopieux5• 1 year ago

This is no secret or suspicion. It is definitely about avoiding (more accuratly, delaying until legislation destroys the business model) the warth of copyright holders with enough lawyers.

I find this very hypocritical given that for all intents and purposes the infringement already happened at training time, since most content wasn't acquired with any form of retribution or attribution (otherwise this entire endeavor would not have been economically worth it). See also the "you're not allowed to plagiarize Disney" being done by all commercial text to image providers.

NoraCodes2• 1 year ago

I don't understand how you can look at behavior like this from the companies selling these systems and conclude that it is ethical for them to do so, or for you to promote their products.

rvz5• 1 year ago

This is just another "rebranding" of the failed "prompt engineering" trend to promote another borderline pseudo-scientific trend to attact more VC money to fund a new pyramid scheme.

Assuming that this will be using the totally flawed MCP protocol, I can only see more cases of data exfiltration attacks on these AI systems just like before [0] [1].

Prompt injection + Data exfiltration is the new social engineering in AI Agents.

[0] https://embracethered.com/blog/posts/2025/security-advisory-...

[1] https://www.bleepingcomputer.com/news/security/zero-click-ai...

Zopieux5• 1 year ago

Rediscovering basic security concepts and hygiene from 2005 is also a very hot AI thing right now, so that tracks.

slavapestov• 1 year ago

I feel like if the first link in your post is a tweet from a tech CEO the rest is unlikely to be insightful.

coderatlarge6• 1 year ago

i don’t disagree with your main point, but is karpathy a tech ceo right now?

simonw42• 1 year ago

I think they meant Tobi Lutke, CEO of Shopify: https://twitter.com/tobi/status/1935533422589399127

CharlieDigital• 1 year ago

I was at a startup that started using OpenAI APIs pretty early (almost 2 years ago now?).

"Back in the day", we had to be very sparing with context to get great results so we really focused on how to build great context. Indexing and retrieval were pretty much our core focus.

Now, even with the larger windows, I find this still to be true.

The moat for most companies is actually their data, data indexing, and data retrieval[0]. Companies that 1) have the data and 2) know how to use that data are going to win.

My analogy is this:

    > The LLM is just an oven; a fantastical oven.  But for it to produce a good product still depends on picking good ingredients, in the right ratio, and preparing them with care.  You hit the bake button, then you still need to finish it off with presentation and decoration.

[0] https://chrlschn.dev/blog/2024/11/on-bakers-ovens-and-ai-sta...

Superbowl5889• 1 year ago

I would assume small context window is blessing in disguise.

You worded it very good.

retinaros• 1 year ago

it is still sending a string of chars and hoping the model outputs something relevant. let’s not do like finance and permanently obfuscate really simple stuff to make us bigger than we are.

prompt engineering/context engineering : stringbuilder

Retrieval augmented generation: search+ adding strings to main string

test time compute: running multiple generation and choosing the best

agents: for loop and some ifs

jumploops• 1 year ago

To anyone who has worked with LLMs extensively, this is obvious.

Single prompts can only get you so far (surprisingly far actually, but then they fall over quickly).

This is actually the reason I built my own chat client (~2 years ago), because I wanted to “fork” and “prune” the context easily; using the hosted interfaces was too opaque.

In the age of (working) tool-use, this starts to resemble agents calling sub-agents, partially to better abstract, but mostly to avoid context pollution.

nomel• 1 year ago

Did you release your client? I've really wanted something like this, from the beginning.

I thought it would also be neat to merge contexts, by maybe mixing summarizations of key points at the merge point, but never tried.

Zopieux5• 1 year ago

I find it hilarious that this is how the original GPT3 UI worked, if you remember, and we're now discussing of reinventing the wheel.

A big textarea, you plug in your prompt, click generate, the completions are added in-line in a different color. You could edit any part, or just append, and click generate again.

90% of contemporary AI engineering these days is reinventing well understood concepts "but for LLMs", or in this case, workarounds for the self-inflicted chat-bubble UI. aistudio makes this slightly less terrible with its edit button on everything, but still not ideal.

surrTurr2• 1 year ago

The original GPT-3 was trained very differently than modern models like GPT-4. For example, the conversational structure of an assistant and user is now built into the models, whereas earlier versions were simply text completion models.

It's surprising that many people view the current AI and large language model advancements as a significant boost in raw intelligence. Instead, it appears to be driven by clever techniques (such as "thinking") and agents built on top of a foundation of simple text completion. Notably, the core text completion component itself hasn’t seen meaningful gains in efficiency or raw intelligence recently...

mgdev• 1 year ago

If we zoom out far enough, and start to put more and more under the execution umbrella of AI, what we're actually describing here is... product development.

You are constructing the set of context, policies, directed attention toward some intentional end, same as it ever was. The difference is you need fewer meat bags to do it, even as your projects get larger and larger.

To me this is wholly encouraging.

Some projects will remain outside what models are capable of, and your role as a human will be to stitch many smaller projects together into the whole. As models grow more capable, that stitching will still happen - just as larger levels.

But as long as humans have imagination, there will always be a role for the human in the process: as the orchestrator of will, and ultimate fitness function for his own creations.

somewhereoutth• 1 year ago

for his own creations.

for their own creations is grammatically valid, and would avoid accusations of sexism!

briangriffinfan2• 1 year ago

After mulling this issue over in my head for a significant amount of time, I've determined that I don't care and he sounds more natural.

GuinansEyebrows2• 1 year ago

i just hope that, along with imagination, humans can have an economy that supports this shift.

pyman3• 1 year ago

That does sound a lot like the role of a software architect. You're setting the direction, defining the constraints, making trade-offs, and stitching different parts together into a working system

jcon3212• 1 year ago

I thought this entire premise was obvious? Does it really take an article and a venn diagram to say you should only provide the relevant content to your LLM when asking a question?

simonw42• 1 year ago

"Relevant content to your LLM when asking a question" is last year's RAG.

If you look at how sophisticated current LLM systems work there is so much more to this.

Just one example: Microsoft open sourced VS Code Copilot Chat today (MIT license). Their prompts are dynamically assembled with tool instructions for various tools based on whether or not they are enabled: https://github.com/microsoft/vscode-copilot-chat/blob/v0.29....

And the autocomplete stuff has a wealth of contextual information included: https://github.com/microsoft/vscode-copilot-chat/blob/v0.29....

  You have access to the following information to help you make
  informed suggestions:

  - recently_viewed_code_snippets: These are code snippets that
  the developer has recently looked at, which might provide
  context or examples relevant to the current task. They are
  listed from oldest to newest, with line numbers in the form
  #| to help you understand the edit diff history. It's
  possible these are entirely irrelevant to the developer's
  change.
  - current_file_content: The content of the file the developer
  is currently working on, providing the broader context of the
  code. Line numbers in the form #| are included to help you
  understand the edit diff history.
  - edit_diff_history: A record of changes made to the code,
  helping you understand the evolution of the code and the
  developer's intentions. These changes are listed from oldest
  to latest. It's possible a lot of old edit diff history is
  entirely irrelevant to the developer's change.
  - area_around_code_to_edit: The context showing the code
  surrounding the section to be edited.
  - cursor position marked as ${CURSOR_TAG}: Indicates where
  the developer's cursor is currently located, which can be
  crucial for understanding what part of the code they are
  focusing on.

mccoyb• 1 year ago

That doesn't strike me as sophisticated, it strikes me as obvious to anyone with a little proficiency in computational thinking and a few days of experience with tool-using LLMs.

The goal is to design a probability distribution to solve your task by taking a complicated probability distribution and conditioning it, and the more detail you put into thinking about ("how to condition for this?" / "when to condition for that?") the better the output you'll see.

(what seems to be meant by "context" is a sequence of these conditioning steps :) )

timr3• 1 year ago

I get what you're saying, but the parent is correct -- most of this stuff is pretty obvious if you spend even an hour thinking about the problem.

For example, while the specifics of the prompts you're highlighting are unique to Copilot, I've basically implemented the same ideas on a project I've been working on, because it was clear from the limitations of these models that sooner rather than later it was going to be necessary to pick and choose amongst tools.

LLM "engineering" is mostly at the same level of technical sophistication that web work was back when we were using CGI with Perl -- "hey guys, what if we make the webserver embed the app server in a subprocess?" "Genius!"

I don't mean that in a negative way, necessarily. It's just...seeing these "LLM thought leaders" talk about this stuff in thinkspeak is a bit like getting a Zed Shaw blogpost from 2007, but fluffed up like SICP.

jcon3212• 1 year ago

Okay fair point, thank you for that info. This makes more sense for the people who are developing prompt generation for some tool (like copilot, claude code, etc) yet I was thinking of it more from a user standpoint (like asking chatgpt, gemini, etc).

alfalfasprout• 1 year ago

The industry has attracted grifters with lots of "<word of the day> engineering" and fancy diagrams for, frankly, pretty obvious ideas

I mean yes, duh, relevant context matters. This is why so much effort was put into things like RAG, vector DBs, prompt synthesis, etc. over the years. LLMs still have pretty abysmal context windows so being efficient matters.

liampulles• 1 year ago

The only engineering going on here is Job Engineering™

ryhanshannon• 1 year ago

It is really funny to see the hyper fixation on relabeling of soft skills / product development to "<blank> Engineering" in the AI space.

bGl2YW5j4• 1 year ago

It undermines the credibility of ideas that probably have more merit than this ridiculous labelling makes it seem!

amelius• 1 year ago

Yes, and it is a soft skill.

zacharyvoase2• 1 year ago

I love how we have such a poor model of how LLMs work (or more aptly don't work) that we are developing an entire alchemical practice around them. Definitely seems healthy for the industry and the species.

simonw42• 1 year ago

The stuff that's showing up under the "context engineering" banner feels a whole lot less alchemical to me than the older prompt engineering tricks.

Alchemical is "you are the world's top expert on marketing, and if you get it right I'll tip you $100, and if you get it wrong a kitten will die".

The techniques in https://www.dbreunig.com/2025/06/26/how-to-fix-your-context.... seem a whole lot more rational to me than that.

zacharyvoase2• 1 year ago

As it gets more rigorous and predictable I suppose you could say it approaches psychology.

__MatrixMan__• 1 year ago

Reminds me of quantum mechanics

hackable_sand3• 1 year ago

This is offensive to alchemy.

geeewhy• 1 year ago

ive beeen experimenting with this for a while, (im sure in a way, most of us did). Would be good to numerate some examples. When it comes to coding, here's a few:

- compile scripts that can grep / compile list of your relevant files as files of interest

- make temp symlinks in relevant repos to each other for documentation generation, pass each documentation collected from respective repos to to enable cross-repo ops to be performed atomically

- build scripts to copy schemas, db ddls, dtos, example records, api specs, contracts (still works better than MCP in most cases)

I found these steps not only help better output but also reduces cost greatly avoiding some "reasoning" hops. I'm sure practice can extend beyond coding.

mpazik• 1 year ago

Yeah, I had similar findings. Making good system can be tedious though when it grows and current tools did not provide enough configuration.

I found opencode, and decided to build a fork that allows to setup specialized agent with custom tools and programmatic context building to organize it better https://github.com/mpazik/openagent

dinvlad3• 1 year ago

I feel like ppl just keep inventing concepts for the same old things, which come down to dancing with the drums around the fire and screaming shamanic incantations :-)

viccis• 1 year ago

When I first used these kinds of methods, I described it along those lines to my friend. I told him I felt like I was summoning a demon and that I had to be careful to do the right incantations with the right words and hope that it followed my commands. I was being a little disparaging with the comment because the engineer in me that wants reliability, repeatability, and rock solid testability struggles with something that's so much less under my control.

God bless the people who give large scale demos of apps built on this stuff. It brings me back to the days of doing vulnerability research and exploitation demos, in which no matter how much you harden your exploits, it's easy for something to go wrong and wind up sputtering and sweating in front of an audience.

emporas• 1 year ago

Prompting sits on the back seat, while context is the driving factor. 100% agree with this.

For programming I don't use any prompts. I give a problem solved already, as a context or example, and I ask it to implement something similar. One sentence or two, and that's it.

Other kind of tasks, like writing, I use prompts, but even then, context and examples are still the driving factor.

In my opinion, we are in an interesting point in history, in which now individuals will need their own personal database. Like companies the last 50 years, which had their own database records of customers, products, prices and so on, now an individual will operate using personal contextual information, saved over a long period of time in wikis or Sqlite rows.

d0gsg0w00f2• 1 year ago

Yes, the other day I was telling a colleague that we all need our own personal context to feed into every model we interact with. You could carry it around on a thumb drive or something.

drmath2• 1 year ago

Isn't "context" just another word for "prompt?" Techniques have become more complex, but they're still just techniques for assembling the token sequences we feed to the transformer.

simonw42• 1 year ago

Almost. It's the current prompt plus the previous prompts and responses in the current conversation.

The idea behind "context engineering" is to help people understand that a prompt these days can be long, and can incorporate a whole bunch of useful things (examples, extra documentation, transcript summaries etc) to help get the desired response.

"Prompt engineering" was meant to mean this too, but the AI influencer crowd redefined it to mean "typing prompts into a chatbot".

drmath2• 1 year ago

Haha there's a pigheaded part of me that insists all of that is the "prompt," but I just read your bit about "inferred definitions," and acceptance is probably a healthier attitude.

bag_boy• 1 year ago

Anecdotally, I’ve found that chatting with Claude about a subject for a bit — coming to an understanding together, then tasking it — produces much better results than starting with an immediate ask.

I’ll usually spend a few minutes going back and forth before making a request.

For some reason, it just feels like this doesn't work as well with ChatGPT or Gemini. It might be my overuse of o3? The latency can wreck the vibe of a conversation.

neilv• 1 year ago

Then you can generate a response.
> Hey Jim! Tomorrow’s packed on my end, back-to-back all day. Thursday AM free if that works for you? Sent an invite, lmk if it works.

Feel free to send generated AI responses like this if you are a sociopath.

joe51504• 1 year ago

Jim's agent replies, "Thursday AM touchbase sounds good, let's circle back after." Both agents meet for a blue sky strategy session while Jim's body floats serenely in a nutrient slurry.

danans• 1 year ago

Came here to say this, too - creepy. Especially when there is no person in the loop, just an LLM agent responding on someone's behalf in their voice.

Roark66• 1 year ago

Isn't the point that it prepares the response, shows it to you along with some context to you. Like a sidebar showing who the other person is with a short summary of your last comms and your calendar. It should let you move the "proposed appointment" in that sidebar calendar and it should update the response to match your choice. If it clashes and you have no time it should show you what those other things are (maybe propose what you could shift) and so on.

This is how I imagine proper AI integration.

What I also want is not sending all my data to the provider. With the model sizes we use these days it's pretty much impossible to run them locally if you want the best, so imo the company that will come up with the best way to secure customer data will win.

stillpointlab• 1 year ago

I've been using the term context engineering for a few months now, I am very happy to see this gain traction.

This new stillpointlab hacker news account is based on the company name I chose to pursue my Context as a Service idea. My belief is that context is going to be the key differentiator in the future. The shortest description I can give to explain Context as a Service (CaaS) is "ETL for AI".

bgwalter2• 1 year ago

These discussions increasingly remind me of gamers discussing various strategies in WoW or similar. Purportedly working strategies found by trial and error and discussed in a language that is only intelligible to the in-group (because no one else is interested).

We are entering a new era of gamification of programming, where the power users force their imaginary strategies on innocent people by selling them to the equally clueless and gaming-addicted management.

coderatlarge6• 1 year ago

i tend to share your view. but then your comment describes a lot of previous cycles of enterprise software selling. it’s just that this time is reaching a little uncomfortably into the builder’s /developer’s traditional areas of influence/control/workflow. how devs feel now is probably how others (ex csr, qa, sre) felt in the past when their managers pushed whatever tooling/practice was becoming popular or sine qua non in previous “waves”.

sarchertech• 1 year ago

This has been happening to developers for years.

25 years ago it was object oriented programming.

mrits• 1 year ago

Tuning the JVM, compiler optimizations, design patterns, agile methodologies, seo , are just a few things that come to mind

Madmallard• 1 year ago

There's quite a lot science that goes into WoW strategizing at this point.

People are using their thinking caps and modelling data.

dysoco• 1 year ago

Purportedly working strategies found by trial and error and discussed in a language that is only intelligible to the in-group

This really does sound like Computer Science since it's very beginnings.

The only difference is that now it's a much more popular field, and not restricted to a few nerds sharing tips over e-mail or bbs.

dawnofdusk• 1 year ago

This really does sound like Computer Science since it's very beginnings.

Except in actual computer science you can prove that your strategies, discovered by trial and error, are actually good. Even though Dijkstra invented his eponymous algorithm by writing on a napkin, it's phrased in the language of mathematics and one can analyze quantitatively its effectiveness and trade-offs, and one can prove if it's optimal (as was done recently).

bigfishrunning• 1 year ago

This really does sound like Computer Science since it's very beginnings.

Dijkstra is rolling in his grave. Computer Science was a rigorous sub-field of mathematics before the tech bros showed up and started moving fast and breaking things. The endless supply of VC money has destroyed this field.

matkoniecz• 1 year ago

only intelligible to the in-group (because no one else is interested)

that applies to basically any domain-specific terminology, from WoW raids through cancer research to computer science and say OpenStreetMap

iammrpayments• 1 year ago

This is basically how online advertising works. Nobody knows how facebook ads works so you still have gurus making money selling senseless advice on how to get lower cost per impression.

tootie2• 1 year ago

It's also the entire SEO industry

nixpulvis• 1 year ago

Having been a computer scientist and avid WoW player, I dislike this take. The best strategies always have a justification.

benreesman9• 1 year ago

The new skill is programming, same as the old skill. To the extent these things are comprehensible, you understand them by writing programs: programs that train them, programs that run inferenve, programs that analyze their behavior. You get the most out of LLMs by knowing how they work in detail.

I had one view of what these things were and how they work, and a bunch of outcomes attached to that. And then I spent a bunch of time training language models in various ways and doing other related upstream and downstream work, and I had a different set of beliefs and outcomes attached to it. The second set of outcomes is much preferable.

I know people really want there to be some different answer, but it remains the case that mastering a programming tool involves implemtenting such, to one degree or another. I've only done medium sophistication ML programming, and my understand is therefore kinda medium, but like compilers, even doing a medium one is the difference between getting good results from a high complexity one and guessing.

Go train an LLM! How do you think Karpathy figured it out? The answer is on his blog!

pyman3• 1 year ago

Saying the best way to understand LLMs is by building one is like saying the best way to understand compilers is by writing one. Technically true, but most people aren't interested in going that deep.

benreesman9• 1 year ago

I don't know, I've heard that meme too but it doesn't track with the number of cool compiler projects on GitHub or that frontpage HN, and while the LLM thing is a lot newer, you see a ton of useful/interesting stuff at the "an individual could do this on their weekends and it would mean they fundamentally know how all the pieces fit together" type stuff.

There will always be a crowd that wants the "master XYZ in 72 hours with this ONE NEAT TRICK" course, and there will always be a..., uh, group of people serving that market need.

But most people? Especially in a place like HN? I think most people know that getting buff involves going to the gym, especially in a place like this. I have a pretty high opinion of the typical person. We're all tempted by the "most people are stupid" meme, but that's because bad interactions are memorable, not because most people are stupid or lazy or whatever. Most people are very smart if they apply themselves, and most people will work very hard if the reward for doing so is reasonably clear.

https://www.youtube.com/shorts/IQmOGlbdn8g

wickedsight• 1 year ago

The best way to understand a car is to build a car. Hardly anyone is going to do that, but we still all use them quite well in our daily lives. In large part because the companies who build them spend time and effort to improve them and take away friction and complexity.

If you want to be an F1 driver it's probably useful to understand almost every part of a car. If you're a delivery driver, it probably isn't, even if you use one 40+ hours a week.

hackable_sand3• 1 year ago

It's not that deep

Davidzheng10• 1 year ago

I highly highly doubt that training a LLM like gpt-2 will help you use something the size of GPT-4. And I guess most people can't afford to train something like GPT-4. I trained some NNs back before the ChatGPT era, I don't think any of it helps in using Chatgpt/alternatives

benreesman9• 1 year ago

With modern high-quality datasets and the plummeting H100 rental costs it is 100% a feasible undertaking for an individual to train a model with performance far closer to gpt-4-1106-preview than to gpt-2, in fact its difficult to train a model that performs as badly as gpt-2 without carefully selecting for datasets like OpenWebText with the explicit purpose of replicating runs of historical interest: modern datasets will do better than that by default.

GPT-4 is a 1.75 terraweight MoE (the rumor has it) and that's probably pushing it for an individual's discretionary budget unless they're very well off, but you don't need to match that exactly to learn how these things fundamentally work.

I think you underestimate how far the technology has come. torch.distributed works out of the box now, deepspeed and other strategies that are both data and model parallel are weekend projects to spin up on an 8xH100 SXM2 interconnected cluster that you can rent from Lambda Labs, HuggingFace has extreme quality curated datasets (the fineweb family I was alluding to from Karpathy's open stuff is stellar).

In just about any version of this you come to understand how tokenizers work (which makes a whole class of failure modes go from baffling to intuitive), how models behave and get evaled after pretraining, after instruct training / SFT rounds, how convergence does and doesn't happen, how tool use and other special tokens get used (and why they are abundant).

And no, doing all that doesn't make Opus 4 completely obvious in all aspects. But its about 1000x more effective as a learning technique than doing prompt engineer astrology. Opus 4 is still a bit mysterious if you don't work at a frontier lab, there's very interesting stuff going on there and I'm squarely speculating how some of that works if I make claims about it.

Models that look and act a lot like GPT-4 while having dramatically lower parameter counts are just completely understood in open source now. The more advanced ones require resources of a startup rather than an individual, but you don't need to eval the same as 1106 to take all the mystery out of how it works.

The "holy shit" models are like 3-4 generations old now.

mountainriver3• 1 year ago

You can give most of the modern LLMs pretty darn good context and they will still fail. Our company has been deep down this path for over 2 years. The context crowd seems oddly in denial about this

arkmm• 1 year ago

What are some examples where you've provided the LLM enough context that it ought to figure out the problem but it's still failing?

mountainriver3• 1 year ago

if prompting worked then we would have reliable multi-step agents, the companies that are succeeding like Manus are doing alignment, which is intuitive

tupac_speedrap• 1 year ago

I mean at some point it is probably easier to do the work without AI and at least then you would actually learn something useful instead of spending hours crafting context to actually get something useful out of an AI.

klardotsh• 1 year ago

Agreed until/unless you end up at one of those bleeding-edge AI-mandate companies (Microsoft is in the news this week as one of them) that will simply PIP you for being a luddite if you aren't meeting AI usage metrics.

mountainriver3• 1 year ago

yes, this is what we found out

ethan_smith• 1 year ago

We've experienced the same - even with perfectly engineered context, our LLMs still hallucinate and make logical errors that no amount of context refinement seems to fix.

hintymad• 1 year ago

The New Skill in AI Is Not Prompting, It's Context Engineering

Sounds like good managers and leaders now have an edge. Per Patty McCord of Netflix fame used to say: All that a manager does is setting the context.

walterfreedom2• 1 year ago

I am mostly focusing in this issue during the development of my agent engine (mostly for game npcs). Its really important to manage the context and not bloat the llm with irrelevant stuff for both quality and inference speed. I wrote about it here if anyone is interested: https://walterfreedom.com/post.html?id=ai-context-management

asciii• 1 year ago

Here I was thinking that part of Prompt Engineering is understanding context and awareness for other yada yada.

joe51504• 1 year ago

Surely Jim is also using an agent. Jim can't be worth having a quick sync with if he's not using his own agent! So then why are these two agents emailing each other back and forth using bizarre, terse office jargon?

zaptheimpaler• 1 year ago

I feel like this is incredibly obvious to anyone who's ever used an LLM or has any concept of how they work. It was equally obvious before this that the "skill" of prompt-engineering was a bunch of hacks that would quickly cease to matter. Basically they have the raw intelligence, you now have to give them the ability to get input and the ability to take actions as output and there's a lot of plumbing to make that happen.

skort• 1 year ago

Yeah, my reaction to this was "Big deal? How is this news to anyone"

It reads like articles put out by consultants at the height of SOA. Someone thought for a few minutes about something and figured it was worth an article.

imiric• 1 year ago

That might be the case, but these tools are marketed as having close to superhuman intelligence, with the strong implication that AGI is right around the corner. It's obvious that engineering work is required to get them to perform certain tasks, which is what the agentic trend is about. What's not so obvious is the fact that getting them to generate correct output requires some special skills or tricks. If these tools were truly intelligent and capable of reasoning, surely they would be able to inform human users when they lack contextual information instead of confidently generating garbage, and their success rate would be greater than 35%[1].

The idea that fixing this is just a matter of providing better training and contextual data, more compute or plumbing, is deeply flawed.

[1] https://www.theregister.com/2025/06/29/ai_agents_fail_a_lot/

LASR2• 1 year ago

Honestly, GPT-4o is all we ever needed to build a complete human-like reasoning system.

I am leading a small team working on a couple of “hard” problems to put the limits of LLMs to the test.

One is an options trader. Not algo / HFT, but simply doing due diligence, monitoring the news and making safe long-term bets.

Another is an online research and purchasing experience for residential real-estate.

Both these tasks, we’ve realized, you don’t even need a reasoning model. In fact, reasoning models are harder to get consistent results from.

What you need is a knowledge base infrastructure and pub-sub for updates. Amortize the learned knowledge across users and you have collaborative self-learning system that exhibits intelligence beyond any one particular user and is agnostic to the level of prompting skills they have.

Stay tuned for a limited alpha in this space. And DM if you’re interested.

bGl2YW5j4• 1 year ago

What you're describing sounds a lot like traditional training of an ML model combined with descriptive+prescriptive analytics. What value do LLMs bring to this use case?

LASR2• 1 year ago

Ability for normal people to set up reasoning chains.

munificent• 1 year ago

All of these blog posts to me read like nerds speedrunning "how to be a tech lead for a non-disastrous internship".

Yes, if you have an over-eager but inexperienced entity that wants nothing more to please you by writing as much code as possible, as the entity's lead, you have to architect a good space where they have all the information they need but can't get easily distracted by nonessential stuff.

tptacek5• 1 year ago

Just to keep some clarity here, this is mostly about writing agents. In agent design, LLM calls are just primitives, a little like how a block cipher transform is just a primitive and not a cryptosystem. Agent designers (like cryptography engineers) carefully manage the inputs and outputs to their primitives, which are then composed and filtered.

dboreham• 1 year ago

The dudes who ran the Oracle of Delphi must have had this problem too.

almosthere• 1 year ago

Which is prompt engineering, since you just ask the LLM for a good context for the next prompt.

aaronlinoops• 1 year ago

As models become more powerful, the ability to communicate effectively with them becomes increasingly important, which is why maintaining context is crucial for better utilizing the model's capabilities.

rTX5CMRXIfFG• 1 year ago

So then for code generation purposes, how is “context engineering” different now from writing technical specs? Providing the LLMs the “right amount of information” means writing specs that cover all states and edge cases. Providing the information “at the right time” means writing composable tech specs that can be interlinked with each other so that you can prompt the LLM with just the specs for the task at hand.

croes• 1 year ago

Next step, solution engineering. Provide the solution so AI can give it to you in nicer words

ninetyninenine2• 1 year ago

We do enough "context engineering" we'll be feeding these companies the training data they need for the AI to build it's own context.

taylorius• 1 year ago

The model starts every conversation as a blank slate, so providing a thorough context regarding the problem you want it to solve seems a fairly obvious preparatory step tbh. How else is it supposed to know what to do? I agree that "prompt" is probably not quite the right word to describe what is necessary though - it feels a bit minimal and brief. "Context engineering" seems a bit overblown, but this is tech. and we do a love a grand title.

Snowfield9571• 1 year ago

What’s it going to be next month?

surrTurr2• 1 year ago

Context engineering will be just another fad, like prompt engineering was. Once the context window problem is solved, nobody will be talking about it any more.

Also, for anyone working with LLMs right now, this is a pretty obvious concept and I'm surprised it's on top of HN.

bravesoul2• 1 year ago

If you have a big enough following you can say the obvious and get a rapturous applause.

aryehof• 1 year ago

Yay, everyone that writes a line of text to an LLM can now claim to be an "engineer".

askonomm• 1 year ago

So ... are we about circled back to realizing why COBOL didn't work yet? This AI magic whispering is getting real close to it just making more sense to "old-school" write programs again.

pvdebbe• 1 year ago

The new AI winter can't come soon enough.

sonicvrooom• 1 year ago

Premises and conclusions.

Prompts and context.

Hopes and expectations.

Black holes and revelations.

We learned to write and then someone wrote novels.

Context, now, is for the AI, really, to overcome dogmas recursively and contiguously.

Wasn't that somebody's slogan someday in the past?

Context over Dogma

Mikejames• 1 year ago

anyone spinning up their own agents at work? internal tools, what’s your stack? workflow? I’m new to this stuff but been writing software for years

defyonce• 1 year ago

at which point AI thing stops being a Stone soup?

https://en.wikipedia.org/wiki/Stone_Soup

You need an expert who knows what to do and how to do it to get good results. Looks like coding with extra steps to me

I DO use AI for some tasks. When I know exactly what I want done and how I want it done. The only issue is busy typing, which AI solves.

walterfreedom2• 1 year ago

AI is already very impressive for natural language formatting and filtering, we use it for ratifying profiles and posts. and it takes around like an hour to implement this from scratch, and there are no alternatives that can do the same thing as comprehensively anyways

pbhjpbhj3• 1 year ago

Attention Is Everything.

To direct attention properly you need the right context for the ML model you're doing inference with.

This inference manipulation -- prompt and/or context engineering -- reminds me of Socrates (as written by Plato) eliciting from a boy seemingly unknown truths [not consciously realised by the boy] by careful construction of the questions.

See Anamnesis, https://en.m.wikipedia.org/wiki/Anamnesis_(philosophy). I'm saying it's like the [Socratic] logical process and _not_ suggesting it's philosophically akin to anamnesis.

0points5• 1 year ago

Only more mental exercises to avoid reading the writing on the wall:

LLM DO NOT REASON !

THEY ARE TOKEN PREDICTION MACHINES

Thank you for your attention in this matter!

__alexs• 1 year ago

A distinction without a difference.

ozgung3• 1 year ago

Why not? What is so special about reasoning that you cannot achieve by predicting tokens aka. constructing sentences?

0points5• 1 year ago

If you don't understand the difference between a LLM and yourself, then you should talk to a therapist, not me.

jedimastert• 1 year ago

Predicting tokens and constructing sentences are not the same thing. It cannot create its own sentences because it does not have a self

LeoPanthera2• 1 year ago

Predicting the next token is reasoning.

0points5• 1 year ago

No, that is statistics.

zurfer2• 1 year ago

What is reasoning? And how is it apparent that LLMs can't reason?

The reality for me is that they are not perfect at reasoning and have many quirks, but it seems to be that they are able to form new conclusions based on provided premises.

Genuinely curious why you think they can't.

0points5• 1 year ago

Genuinely curious why you think they can't.

Show me _ANY_ example of novel thought by a LLM.

pennaMan2• 1 year ago

prediction is the result of reasoning

0points5• 1 year ago

No it's not.

Prediction is the ability to predict something.

Reasoning is the ability to reason.

imhoguy• 1 year ago

What LLMs lack is emotions, because thanks to emotions people build great fortresses (fear, insecurity) or break limits (courage, risk).

easyThrowaway• 1 year ago

"Any sufficiently advanced prediction is indistinguishable from reasoning" (/s... maybe.)

silveraxe93• 1 year ago

It's ironic how people write this without a shred of reasoning. This is just _wrong_. LLMs are not simply token prediction machines since GPT-3.

During pre-training, yeah they are. But there's a ton of RL being done on top after that.

If you want to argue that they can't reason, hey fair be my guest. But this argument keeps getting repeated as a central reason and it's just not true for years.

bearjaws• 1 year ago

Every time I read something like this, I just imagine it in "old man yells at cloud" meme format.

Just because it is not reasoning doesn't mean it can't be quite good at its tasks.

grey-area• 1 year ago

the constant switches in justification for why GAI isn't quite there yet really remind me of the multiple switches of purpose for blockchains as VC funded startups desperately flailed around looking for something with utility.

_Algernon_• 1 year ago

The prompt alchemists found a new buzzword to try to hook into the legitimacy of actual engineering disciplines.

megalord• 1 year ago

I agree with everything in the blog post. What I'm struggling with right now is the correct way of executing things the most safe way but also I want flexibility for LLM. Execute/choose function from list of available fns is okay for most use cases, but when there is something more complex, we need to somehow execute more things from allowed list, do some computations in between calls etc.

noobermin• 1 year ago

Once again, all the hypsters need to explain to me how than just programming yourself. I don't need to (re-)craft my context, it's already in my head.

pg said a few months ago on twitter that ai coding is just proof we need better abstract interfaces, perhaps, not necessarily that ai coding is the future. The "conversation is shifting from blah blah to bloo bloo" makes me suspicious that people are trying just to salvage things. The provided examples are neither convincing nor enlightening to me at all. If anything, it just provides more evidence for "just doing it yourself is easier."

jhrmnn• 1 year ago

When we write source code for compilers and interpreters, we “engineer context” for them.

kachapopopow• 1 year ago

I'll quote myself since it seems oddly familiar:

---

Forget AI "code", every single request will be processed BY AI! People aren't thinking far enough, why bother with programming at all when an AI can just do it?

It's very narrow to think that we will even need these 'programmed' applications in the future. Who needs operating systems and all that when all of it can just be AI.

In the future we don't even need hardware specifications since we can just train the AI to figure it out! Just plug inputs and outputs from a central motherboard to a memory slot.

Actually forget all that, it'll just be a magic box that takes any kind of input and spits out an output that you want!

quonn• 1 year ago

Is this sarcasm or not?

edit: Yes it is.

1oooqooq• 1 year ago

why stop on what you want? plug your synapses and chemical receptors and let it also figure that out *thumbsupemoji

theasisa• 1 year ago

This reminds me of the talk The Birth And Death Of JavaScript, https://www.destroyallsoftware.com/talks/the-birth-and-death...

jeremyjh• 1 year ago

How does the AI open and close circuits without machine code?

Answer: Its AI all the way down.

grumple2• 1 year ago

After a recent conversation here, I spent a few weeks using agents.

These agents are just as disappointing as what we had before. Except now I waste more time getting bad results, though I’m really impressed by how these agents manage to fuck things up.

My new way of using them is to just go back to writing all the code myself. It’s less of a headache.

simonw42• 1 year ago

Which definition of "agents" are you using there, and which ones did you try?

grumple2• 1 year ago

Cursor, Copilot agent mode, and Windsurf. The agent modes can search repos, modify code, and run code on their own. I thought Cursor's agent was the best. I did like Windsurf's agent plans, but the actual results weren't good. Copilot's agent has been slow and not good. But basically all of them were like a pretty bad junior engineer - sometimes it would hit the right result, but usually not. The code often looked good but rarely even ran, let alone met requirements. They would frequently break things, I'd fix them, they'd break them again. Most of the time this cycle was slower and more frustrating than just writing the code myself. I tried one or two one-shots on lovable - the design was impressive, but functionality and attention to specs were poor.

I've had the most success with extremely small questions rather than asking the agents to write a lot of code. In those cases, the code is still usually wrong, but it's close or small enough that I can quickly fix it.

Don't get me wrong: I find all of these tools to be really impressive and good enough to be useful. But the improvements and huge productivity gains friends claim they or their workers are getting just aren't materializing for me.

bmiekre• 1 year ago

It’s kind of funny hearing everyone argue over what engineering means.

HarHarVeryFunny• 1 year ago

I guess "context engineering" is a more encompassing term than "prompt engineering", but at the end of the day it's the same thing - choosing the best LLM input (whether you call it context or a prompt) to elicit the response you are hoping for.

The concept of prompting - asking an Oracle a question - was always a bit limited since it means you're really leaning on the LLM itself - the trained weights - to provide all the context you didn't explicitly mention in the prompt, and relying on the LLM to be able to generate coherently based on the sliced and blended mix of StackOverflow and Reddit/etc it was trained on. If you are using an LLM for code generation then obviously you can expect a better result if you feed it the API docs you want it to use, your code base, your project documents, etc, etc (i.e "context engineering").

Another term that has recently been added to the LLM lexicon is "context rot", which is quite a useful concept. When you use the LLM to generate, it's output is of course appended to the initial input, and over extended bouts of attempted reasoning, with backtracking etc, the clarity of the context is going to suffer ("rot") and eventually the LLM will start to fail in GIGO fashion (garbage-in => garbage-out). Your best recourse at this point is to clear the context and start over.

Havoc• 1 year ago

Honestly this whole "context engineering" trend/phrase feels like something a Thought Leader on Linkedin came up with. With a sprinkling of crypto bro vibes on top.

Sure it matters on a technical level - as always garbage in garbage out holds true - but I can't take this "the art of the" stuff seriously.

niemandhier• 1 year ago

LLM agents remind me of the great Nolan movie „Memento“.

The agents cannot change their internal state hence they change the encompassing system.

They do this by injecting information into it in such a way that the reaction that is triggered in them compensates for their immutability.

For this reason I call my agents „Sammy Jenkins“.

StochasticLi2• 1 year ago

I think we can reasonably expect they will become non-stateless in the next few years.

roflyear• 1 year ago

Why?

tdaltonc• 1 year ago

If agents are stateful a few years form now it will be because they accrete a layer of context engineering.

thatthatis• 1 year ago

Glad we have a name for this. I had been calling it “context shaping” in my head for a bit now.

I think good context engineering will be one of the most important pieces of the tooling that will turn “raw model power” into incredible outcomes.

Model power is one thing, model power plus the tools to use it will be quite another.

Davidzheng10• 1 year ago

Let's grant that context engineering is here to stay and that we can never have context lengths be large enough to throw everything in it indiscriminately. Why is this not a perfect palce to train another AI whose job is to provide the context for the main AI?

blensor• 1 year ago

Just yesterday I was thinking if we need a code comment system that separates intentional comments from ai note/thoughts comments when working in the same files.

I don't want to delete all thoughts right away as it makes it easier for the AI to continue but I also don't want to weed trhough endless superfluous comments

lifeisstillgood• 1 year ago

Something that strikes me, is that (the whole point of this thread is) if I want two LLMs to “have a conversation” or to work together as agents on similar problems we need to have same or similar context.

And to drag this back to politics - that kind of suggests that when we have political polarisation we just have context that are so different the LLM cannot arrive at similar conclusions

I guess it is obvious but it is also interesting

simonw42• 1 year ago

One of the most valuable techniques for building useful LLM systems right now is actually the opposite of that.

Context is limited in length and too much stuff in the context can lead to confusion and poor results - the solution to that is "sub-agents", where a coordinating LLM prepares a smaller context and task for another LLM and effectively treats it as a tool call.

The best explanation of that pattern right now is this from Anthropic: https://www.anthropic.com/engineering/built-multi-agent-rese...

PaulRobinson• 1 year ago

Shared context is critical to working towards a common goal. It's as true in society when deciding policy, as it is in your vibe coded match-3 game for figuring out what tests need to be written.

TrackerFF• 1 year ago

It is probably 6-7 months ago I used ChatGPT for "vibe coding", and my main complaint was that the model eventually started moving away too far from its intended goal, as and it eventually go lost and stuck in some loop. In which case I had to fire up a new model, and feed all the context I had, and continue.

A couple of days ago I fired up o4-mini-high, and I was blown away how long it can remember things, how much context it can keep up with. Yesterday I had a solid 7 hour session with no reloads or anything. The source files were regularly 200-300 LOC, and the project had 15 such files. Granted, I couldn't feed more than 10 files into, but it managed well enough.

My main domain is data science, but this was the first time I truly felt like I could build a workable product in languages I have zero knowledge with (React + Node).

And mind you, this approach was probably at the lowest level of sophistication. I'm sure there are tools that are better suited for this kind of work - but it did the trick for me.

So my assessment of yesterdays sessions is that:

- It can handle much more input.

- It remembers much longer. I could reference things provided hours ago / many many iterations ago, but it still kept focus.

- Providing images as context worked remarkably well. I'd take screenshots, edit in my wishes, and it would provide that.

jm42• 1 year ago

I went down that rabbit hole with Cursor and it's pretty good. Then I tried tools like Cline with Sonnet 4 and Claude Code. The Anthropic models have huge context and it shows. I'm no expert, but it feels like you reach a point where the model is good enough and then the gains are coming from the context size. When I'm doing something complex, I'm filling up the 200k context window and getting solutions that I just can't get from Cursor or ChatGPT.

I had a data wrangling task where I determine the value of a column in a dataframe based on values in several other columns. I implemented some rules to do the matching and it worked for most of the records, but there are some data quality issues. I asked Claude Code to implement a hybrid approach with rules and ML. We discussed some features and weighting. Then, it reviewed my whole project, built the model and integrated it into what I already had. The finished process uses my rules to classify records, trains the model on those and then uses the model to classify the rest of them.

Someone had been doing this work manually before and the automated version produces a 99.3% match. AI spent a few minutes implementing this at a cost of a couple dollars and the program runs in about a minute compared to like 4 hours for the manual process it's replacing.

hackable_sand3• 1 year ago

Recently cancelled my Cursor subscription because it's pretty bad.

yummybear• 1 year ago

Amazing to see people try to reinvent communication skills.

insane_dreamer• 1 year ago

Semantics. The context is actually part of the "prompt". Sure we can call it "context engineering" instead of "prompt engineering", where now the "prompt" is part of the "context" (instead of the "context" being part of the "prompt") but it's essentially the same thing.

tom_m• 1 year ago

That is prompting. It's all a prompt going in. The parts you see or don't see as an end user is just UX. Of course when you obscure things, it changes the UX for the better or the worse.

HardCodedBias• 1 year ago

The central argument is that the importance of prompt "tricks"—essentially empirical workarounds for current LLM limitations will decline as the technology matures.

The truly valuable and future-proof skill is "context engineering". This focuses on providing the LLM with the information required to reason through the task at hand. Although current LLMs present a trade-off between the size of the context and the quality of the output, this is a constraint that we can expect to lessen with future advancements.

m3kw93• 1 year ago

Context “engineering” likely should involve knowing how the llm treats context size, say needle in hay stack performance, how context size affect hallucination rate, when to summerize context instead of entering the full thing.

m3kw93• 1 year ago

Just like the phasing out of prompt engineering, context engineering will phase out in around 6 months

linguistbreaker• 1 year ago

Prompt engineering was just trying to fit all the context into one prompt - but actually there would often be a series of prompts both positive and negative so...

I get coining a new term and that can be useful in itself but I don't see a big conceptual jump here.

simonw42• 1 year ago

It's not a big contextual jump. It's trying to solve for the problem where a lot of people think "prompt engineering" means "typing a prompt into a chatbot" - and the related problem that many people haven't yet realized you can (and should) dump documents, examples and other long-form content into an LLM to get good results.

0xfaded• 1 year ago

At work we've licensed cursor, but as a vim holdout it's a nogo and we're otherwise somewhat restricted on what we can install.

I have 3 vim commands:

ZB $n: paste the buffer $n inside backticks along with the file path.

Z: Run the current buffer through our llm and append the output

ZI: Run the yank register through our llm and insert the output at the cursor.

The commands also pass along my AGENTS.md

Basically I'm manually building the context. One thing I really like is that when it outputs something stupid, I can just edit that part. E.g. if I ask for a plan to do something, and I don't like step 5, I can just delete it.

One humorous side effect is that without the clear chat structure, it sometimes has difficulty figuring out the end-of-stream. It can end with a question like "would you like me to ...?", answer itself yes, and keep going.

mrhillsman• 1 year ago

Hi everyone,

After working on something related for some months now I would like to put it out there based on the considerable attention being put towards "context engineering". I am proposing the *Context Window Architecture (CWA)* – a conceptual reference architecture to bring engineering discipline to LLM prompt construction. Would love for others to participate and provide feedback. A reference implementation where CWA is used in a real-world/pragmatic scenario could be great to tease out more regarding context engineering and if CWA is useful. Additionally I am no expert by far so feedback and collaboration would be awesome.

Blog post: https://mrhillsman.com/posts/context-engineering-realized-co...

Proposal via Google Doc: https://docs.google.com/document/d/1qR9qa00eW8ud0x7yoP2XicH3...

mumbisChungo• 1 year ago

context engineering, tool development, and orchestration

ie. the new skill in AI is complex software development

daxfohl4• 1 year ago

Seems like there'd be an opportunity for open source tooling here. Context visualized, summarizers, explorers, A/B testers, etc. Also LLM pre-caching of context summaries since IIUC any context change requires full N^2 recalculation of everything so adds a ton of latency and cost. And some optimizers since the previous N is actually (N-M) where M is the the first 0..M context tokens that were unchanged by your update. Though generally you probably want to summarize more of the beginning of the context, so M is likely small in most cases.

Anyway, seems like most of these algorithms are fairly ad hoc things built into all the various agents themselves these days, and not something that exist in their own right. Seems like an opportunity to make this it's own ecosystem, where context tools can be swapped and used independently of the agents that use them, similar to the LLMs themselves.

bsoles• 1 year ago

"the art of providing all the context for the task to be plausibly solvable by the LLM.”

And who is going to do that? The "context engineer", who doesn't know anything about the subject and runs to the LLM for quick answers without having any ability to evaluate if the answer is solid or not?

We saw the same story with "data scientists". A general understanding of tools with no understanding of the specific application areas is bound to result in crappy products, if not in business disasters.

Leo-thorne• 1 year ago

I learned this the hard way. Even a great prompt won't work if the context window is off. If key information is missing or important history is buried too deep, the model will still fail. Now I always explain the problem clearly and set the scene before letting the AI take over.

byannafilip• 1 year ago

i actually found that there is a startup actually doing that called theo growth, they are tackling the problem of missing this context layer

iszomer• 1 year ago

This article reads as if the author pulled this idea straight from the MGS2's ending regarding giving and creating context..

mrcoders• 1 year ago

Looking at this thread, excited to see the community articulating what many of us have been experiencing. The shift from "prompt engineering" to "context engineering" really captures what's happening. The technical stuff (RAG, vector databases) is getting commoditized. But there's this foundational knowledge organization layer that's becoming critical.

Most companies have context scattered across wikis, Slack, Google Docs. You can build sophisticated retrieval systems, but if you're feeding them fragmented information, you're missing huge optimization opportunities.

There's research backing this - Microsoft/Salesforce found 39% accuracy drop in multi-turn conversations - so for the agent interaction's it's even more cirtical to give 'just enough' context (https://arxiv.org/pdf/2505.06120).

When (business) context is properly structured upfront, you minimize those patterns.

damnever• 1 year ago

It is still "prompting".

The new skill in AI is not prompting, it's context engineering

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