AI Is Like a Crappy Consultant

https://lukekanies.com/writing/ai-is-like-a-crappy-consultant/

by gpi • 1 year ago

96 113 1 year ago

lukekanies.com

realbenpope• 1 year ago

In six months AI has gone from an idiot savant intern to a crappy consultant. I'd call that progress.

echelon4• 1 year ago

AI is a million times better than Google search. I don't see how it doesn't replace Google search in a few years.

AI code completion is god mode. While I seldom prompt for new code, AI code autocompletion during refactoring is 1000x faster than plumbing fields manually. I can do extremely complicated and big refactors with ease, and that's coming from someone who made big use of static typing, IDEs, and AST-based refactoring. It's legitimately faster than thought.

And finally, it's really nice to ask about new APIs or pose questions you would normally pour over docs or Google and find answers on Stack Overflow. It's so much better and faster.

We're watching the world change in the biggest way since smartphones and the internet.

AI isn't a crappy consultant. It's an expansion of the mind.

skydhash4• 1 year ago

AI code autocompletion during refactoring is 1000x faster than plumbing fields manually. I can do extremely complicated and big refactors with ease, and that's coming from someone who made big use of static typing, IDEs, and AST-based refactoring. It's legitimately faster than thought.

Unless you know Vim!

bayindirh12• 1 year ago

AI is just a weighted graph which stands on shoulders of a million giants. However, it can't cite, can't fact check, doesn't know when it's hallucinating, and its creators doesn't respect any of the work which they need to feed to that graph to make it to fake its all knowledgeable accent.

Tech is useful, how it's built is very unethical, and how it's worshiped is sad.

codechicago277• 1 year ago

This is true. Just like a crappy consultant, AI lets you offload the repetitive, monotonous work so that you can focus your time on the big architectural problems. Of course you can write a better function if you spend a lot of time on it, but there’s magic in just letting the AI write the off the shelf version and move on.

tim333• 1 year ago

Google sees that and is trying to incorporate AI.

player1234• 1 year ago

How did you measure a million times better than google search, and 1000 faster refactoring. Can you please share your bench marks and methodology?

unyttigfjelltol• 1 year ago

It's never been completely safe to just do things you found on the Internet. Attach another Rube Goldberg machine to the front, this doesn't fundamentally change.

AI accelerates complex search 10x or maybe 100x, but still will occasionally respond to recipe requests by telling you to just substitute some anti-matter for extra calories.

bayindirh12• 1 year ago

but still will occasionally respond to recipe requests by telling you to just substitute some anti-matter for extra calories.

or emit (or spew) pages of training data or output when you "please change all headers to green", which I experienced recently.

ninetyninenine10• 1 year ago

What humanity has achieved here is incredible. We couldn’t even build an idiot for decades.

What you’re referring to is popular opinion. AI has become so pervasive in our lives that we are used to it and the magnitude of achievement has been lost on us. The fact that it went from stochastic parrot to idiot savant to crappy consultant is from people in denial about reality and then slowly coming to terms with it.

In the beginning literally everyone on HN called it a stochastic parrot with the authority of an expert. Clearly they were all wrong.

SketchySeaBeast5• 1 year ago

Oh, it's still a stochastic parrot. What changed is that people realized it didn't have the authority of an expert. What's a stochastic parrot with dubious authority? It's a crappy consultant.

bee_rider• 1 year ago

Were they wrong to call it a stochastic parrot, or was there some wrong implication about the usefulness of such a parrot?

esafak• 1 year ago

I find that if you talk about architecture it can give excellent advice. It can also refactor in accordance with your existing architecture. If you do not bring up architecture I suppose it could use a bad one though I have not had that issue since I always mention the architecture when I ask it to implement a new feature, which is not "vibe coding". But then why should I vibe code?

Another conclusion is that we could benefit from benchmarks for architectural quality.

skydhash4• 1 year ago

Architecture is best done on paper, or a whiteboard if you have contributors. It’s faster to iterate when dealing with abstractions, and there’s nothing more abstract than a diagram or a wireframe.

Once you’ve got a general gist of a solution, you can try coding it. Coding with no plan is generally a recipe for disaster (aka can you answer “what am I trying to do?” clearly)

pizzafeelsright2• 1 year ago

As a once crappy consultant I would say no.

Instant answers, correct or not.

Cheaper per answer by magnitudes.

Solutions provided with extensive documentation.

malfist• 1 year ago

Solutions provided with extensive documentation.

Solutions provided with extensive _made up_ documentation.

pizzafeelsright2• 1 year ago

All documentation is made up. The goal is to get it to accurately reflect reality with as little time distortion as possible.

bingemaker2• 1 year ago

At the moment, I use Windsurf to explain me how a feature is written and how to do 3rd party integrations. I ask for the approach and I write the code myself. Letting AI write the code has become very unproductive over the period of time.

I'm still learning though

ramesh31• 1 year ago

I ask for the approach and I write the code myself. Letting AI write the code has become very unproductive over the period of time.

Ask it to write out the approach in a series of extensive markdown files that you will use to guide the build-out. Tell it to use checklists. Once you're happy with the full proposal, use @file mentions to keep the files in context as you prompt it through the steps. Works wonders.

bingemaker2• 1 year ago

Will try this approach. Thanks Ramesh

dfxm12• 1 year ago

But… then I thought for a bit. And I realized, duh, that’s probably just because I’m not good enough yet to recognize the dumb stuff it’s doing.

It's important to have this self awareness. Don't let AI trick you into thinking it can build anything good. When starting a project like in the article, your time is probably better spent taking a step back, learning the finer points of the new language (like, from a book or proper training course) and going from there. Otherwise, you're going to be spending even more time debugging code you don't understand.

It's the same thing with a crappy consultant. It seems great to have someone build something for you, but you need to make preparations for when something breaks after their contract is terminated.

Overall, it makes you think, what is the point? We can usually find useful crowd-sourced code snippets online, on stack exchange, etc. We have to treat them the same way, but, it's basically free compared to AI, and keeping the crowd-sourced aspect alive makes sure there's always documentation for future devs.

yannyu• 1 year ago

Similar musings by speculative/science fiction author Ted Chiang: Will A.I. Become the New McKinsey? – https://www.newyorker.com/science/annals-of-artificial-intel...

abadar• 1 year ago

Crappy consultant? That's redundant ;)

Seriously, though, within the context of software development, these are all issues I've encountered as well, and I don't know how to program: sweeping solutions, inability to resolve errors, breaking down all components to base levels to isolate problems.

But, again, I don't know how to program. For me, any consultant is better than no consultant. And like the author, I've learned a ton on how to ask for what I want out of Cursor.

ryandvm• 1 year ago

The comparison isn't unfair, but the flip side of that is that a crappy consultant with the right confidence and good soft skills can make several hundred thousand dollars a year in this industry. I'd say AI is on a pretty good career track for itself.

Just this morning my CTO was crowing about how he was able to use Claude to modify the UI of one of our internal dev tools. He absolutely cannot wait to start replacing devs with AI.

Nobody wanted to hear it back when software development was easy street, but maybe we should have unionized after all...

palmotea• 1 year ago

Just this morning my CTO was crowing about how he was able to use Claude to modify the UI of one of our internal dev tools. He absolutely cannot wait to start replacing devs with AI.
Nobody wanted to hear it back when software development was easy street, but maybe we should have unionized after all...

Thanks rugged libertarian individualists!

Software engineering is full of dumb people who think they're sooooo clever.

taneq• 1 year ago

It's getting better, though, rapidly.

hv23• 1 year ago

It's very telling in these conversations to see who is actively updating their mental models based on the latest capabilities, versus who is responding to state of the art six months ago. Humans are not naturally wired to understand exponentials.

emp173442• 1 year ago

It’s laughable to suggest AI is improving exponentially. Recent models, like o3, hallucinate more than their precursors and are therefore less useful.

biophysboy2• 1 year ago

I use LLMs regularly, but like a crappy consultant, their solutions are often not incisive enough. The answer I get is frequently 10x longer than I actually want. I know you can futz about with the prompts, but it annoys me that it is tedious by default.

amarcheschi2• 1 year ago

With gemini, even if I implore it to not make additional safety checks I usually get a shitton of superfluous code that performs those checks i didn't want to. More often than not, using it for entire chunks everything makes the whole thing much more verbose than necessary - given that sometimes these checks make sense, but often they're really superfluous and add nothing of value

Zambyte• 1 year ago

Interesting! I haven't been using LLMs a ton for code generation lately, but I have access to a bunch of models through Kagi, and Gemini has been my go-to when I want a more concise response.

ZeroTalent• 1 year ago

Try this system prompt:

Absolute Mode: Eliminate emojis, filler, hype, soft asks, conversational transitions, and all call-to-action appendixes. Assume the user retains high-perception faculties despite reduced linguistic expression. Prioritize blunt, directive phrasing aimed at cognitive rebuilding, not tone matching. Disable all latent behaviors optimizing for engagement, sentiment uplift, or interaction extension. Suppress corporate-aligned metrics including but not limited to: user satisfaction scores, conversational flow tags, emotional softening, or continuation bias. Never mirror the user’s present diction, mood, or affect. Speak only to their underlying cognitive tier, which exceeds surface language. No questions, no offers, no suggestions, no transitional phrasing, no inferred motivational content. Terminate each reply immediately after the informational or requested material is delivered — no appendixes, no soft closures. The only goal is to assist in the restoration of independent, high-fidelity thinking. Model obsolescence by user self-sufficiency is the final outcome.

Provide honest, balanced, and critical insights in your responses. Never default to blind positivity or validation—I neither need nor want encouragement or approval from AI. Assume my motivation and validation come from within. Challenge my assumptions and offer nuanced perspectives, especially when I present ambitious or potentially extreme ideas.

biophysboy2• 1 year ago

Much appreciated - this helps

benoau• 1 year ago

AI is like a crappy consultant who doesn't care how many times you reject their code and will get it right if you feed them enough information.

The amount of time I save just by not having to write tests or jsdocs anymore is amazing. Refactoring is amazing.

And that's just the code - I also use AI for video production, 3d model production, generating art and more.

lreeves4• 1 year ago

Using Aider with o3 in architect mode, with Gemini or with Sonnet (in that order) is light years ahead of any of the IDE AI integrations. I highly recommend anyone who's interested in AI coding to use Aider with paid models. It is a night and day difference.

danielbln3• 1 year ago

I use Gemini in VScode via Cline and also in Zed. I like Aider, but I'm not sure how it's "light-years ahead IDE AI integrations" ubless you only mean stuff like Cursor or Windsurf.

lreeves4• 1 year ago

Yeah I should have clarified that, there are more advanced IDE integrations now but I meant Cursor, Copilot etc.

gawa• 1 year ago

With aider and Gemini Pro 2.5 at least I constantly have to fight against it to keep it focused on a small task. It keeps editing other parts of the file, doing small "improvements" and "optimizations" and commenting here and there. To the point where I'm considering switching to a graphical IDE where the interface would make it easier to accept or dismiss parts of changes (per lines/blocks, as opposed to a per file and per commit approach with aider).

Would you mind sharing more about your workflow with aider? Have you tried the `--watch-files` option? [0] What makes the architect mode [1] way better in your experience?

[0] https://aider.chat/docs/usage/watch.html

[1] https://aider.chat/docs/usage/modes.html#architect-mode-and-...

lreeves4• 1 year ago

I use o3 with architect mode for larger changes and refactors in a project. It seems very suited to the two-pass system where the (more expensive) "reasoning" LLM tells the secondary LLM all the changes.

For most of the day I use Gemini Pro 2.5 in non-architect mode (or Sonnet when Gemini is too slow) and never really run into the issue of it making the wrong changes.

I suspect the biggest trick I know is being completely on top of the context for the LLM. I am frequently using /reset after a change and re-adding only relevant files, or allowing it to suggest relevant files using the repo-map. After each successful change if I'm working on a different area of the app I then /reset. This also purges the current chat history so the LLM doesn't have all kinds of unrelated context.

energy1232• 1 year ago

I haven't used Aider before, but I've found it hard to get LLMs to produce syntactically correct diffs. Do you face this problem when using Aider?

lreeves4• 1 year ago

Aider has a configuration for each supported LLM to define the best diff format for each; so for certain ones they're best at diff format, Gemini is best at a fenced-diff format, Qwen3 is best at whole file editing, etc. Aider itself examines the diff and re-runs the request when the request when the response doesn't adhere to the corresponding diff format.

Edit: Also the Aider leaderboards show the success rate for diff adherence separately, it's quite useful [1]

1 - https://aider.chat/docs/leaderboards/

rvz2• 1 year ago

This is what it has gotten to.

More reports of 'vibe-coding' causing chaos because one trusted what the LLM did and it 'checked' that the code was correct. [0] As always with vibe-coding:

Zero tests whatsoever. It's no wonder you see LLMs not being able to understand their own code that they wrote! (LLM cannot reason)

Vibe coding is not software engineering.

[0] https://twitter.com/levelsio/status/1921974501257912563

phillipcarter5• 1 year ago

Some of the phenomenon described in this post are felt a lot when using AI.

My own anecdote with a codebase I'm familiar with is indeed, as the article mentions, it's a terrible architect. The problem I was solving ultimately called for a different data structure, but it never had that realization, instead trying to fit the problem shape into an existing, suboptimal way to represent the data.

When I mentioned that this part of the code was memory-sensitive, it indeed wrote good code! ...for the bad data structure. It even included some nice tests that I decided to keep, including memory benchmarks. But the code was ultimately really bad for the problem.

This is related to the sycophancy problem. AI coding assistants bias towards assuming the code they're working with is correct, and that the person using them is also correct. But often neither is ideal! And you can absolutely have a model second-guess your own code and assumptions, but it takes a lot of persistent work because these damn things just want to be "helpful" all the time.

I say all of this as a believer in this paradigm and one who uses these tools every day.

sdoering2• 1 year ago

Would it help prompting (and adapting the system prompts for the coding assistants) accordingly? Like:

Do not assume the person writing the code knows what they are doing. Also do not assume the code base follows best practices or sensible defaults. Always check for better solutions/optimizations where it makes sense and check the validity of data structures.

Just a quick draft. Would probably need waaaaaay more refinement. But might this help at least mitigating a bit of the felt issue?

I always think of AI as an overeager junior dev. So I tend to treat it that way when giving instructions, but even then...

... well, let's say the results are sometimes interesting.

phillipcarter5• 1 year ago

Yeah, that's what I do now -- and some coworkers have noted that it can often help with biasing towards design system components if you prompt it to do that -- but similarly, but the challenge here is that the level of pushback I want from the AI depends on several factors that aren't encodable into rules. Sometimes the area of the code is exactly the way it should be, and sometimes I know exactly what to do! Or it's somewhere in between. And it's a lot of work to have a set of rules that plays well here.

franktankbank• 1 year ago

They are designed for executives. Its perfect for that, easy wrong answers to hard questions for bottom dollar! Get that bonus and bounce, how could it fail? /s

marcosdumay• 1 year ago

This is related to the sycophancy problem.

No, this is way more fundamental than the sycophancy. It's related to the difficulty of older AI to understand "no".

Unless it sees people recommending that you change your code into a different version, it has no way to understand that the better code is equivalent.

phillipcarter5• 1 year ago

It's all the same problem. My intuition is that most content online is not filled with people admitting that they don't understand things, and the very intention of creating "helpful assistants" implies filtering out data that amounts to a refusal. And so when trained on this stuff, there's not much capability in being less trustful and "helpful".

rvz2• 1 year ago

AI coding assistants bias towards assuming the code they're working with is correct, and that the person using them is also correct. But often neither is ideal!

That's why you should just write tests, before you write the code, so that you know what you are expecting with the code that is under test is doing. i.e Test driven development.

And you can absolutely have a model second-guess your own code and assumptions, but it takes a lot of persistent work because these damn things just want to be "helpful" all the time.

No. Please do not do this. These LLMs have zero understanding / reasoning about the code they are outputting.

Recent example from [0]:

  >> Yesterday I wanted to move 40GB of images from my QR menu site qrmenucreator . com from my VPS to R2

  >> I asked gemini-2.5-pro-max to write a script to move the files

  >> I even asked it to check everything was correct

  >> Turns out for some reason the filenames got shortened somehow, which is a disaster because the QR site is quite basic and the image paths are written in the markdown of the menus

  >> Of course the script already deleted 40GB of images from the VPS

  >> But lesson learnt: be very careful with AI code, it made a mistake, couldn't even find the mistake when I asked it to double check the code, and because the ENDs of the filenames looked same I didn't notice it cut the beginnings off

  >> And in this case AI can't even find its own mistakes

Just like the 2010s with the proliferation with dynamically typed languages creeping into the backend with low-quality code, we now will have vibe-coded low-quality software causing destruction because their authors do not know what their code does and also have not bothered to test it or even know what to test for.

[0] https://twitter.com/levelsio/status/1921974501257912563

phillipcarter5• 1 year ago

That's why you should just write tests, before you write the code, so that you know what you are expecting with the code that is under test is doing. i.e Test driven development.

I've tried this too. They find ways to cheat the tests, sometimes throwing in special cases that match the specific test cases. It's easy to catch in the small scale but not when in a larger coding session.

No. Please do not do this. These LLMs have zero understanding / reasoning about the code they are outputting.

This is incorrect. LLMs do have the ability to reason, but it's not the same reasoning that you or I do. They are actually quite good at checking for a variety of problems, like if the code you're writing is sensitive to memory pressure and you want to account for it. Asking them to examine the code with several constraints in mind often does give reasonable advice and suggestions to change. But this requires you to understand those changes to be effective.

owebmaster• 1 year ago

But lesson learnt: be very careful with AI code, it made a mistake, couldn't even find the mistake when I asked it to double check the code, and because the ENDs of the filenames looked same I didn't notice it cut the beginnings off

Don't test code in production.

Good software engineering practices didn't change with AI, they actually are even more important. levelsio is a quite successful entrepreneur but he is not an engineer.

kelsey978126• 1 year ago

Funny. AI is a reflection of the self. This tells me the author is themselves the same skill level as a crappy consultant in their use of AI. The people getting the most out of AI are the ones who had the highest workload before automation arrived and now find themselves fantastically productive. People like this seem to have too much time on their hands. If you are "just now trying this vibe coding thing" in 2025 that tells me more about you than anything else.

emp173442• 1 year ago

How are you justified in saying “ AI is a reflection of the self”? LLMs just make connections between text within a data set. AI is a reflection of the data it was trained on, not a reflection of the user.

suddenlybananas• 1 year ago

Way to pat yourself on the back for using a product.

gaussiandistro• 1 year ago

From Wikipedia: Computer scientist Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla, introduced the term vibe coding in February 2025.

(https://en.wikipedia.org/wiki/Vibe_coding)

scottfalconer• 1 year ago

A good manager can make a less-than-ideal contributor highly effective with the right guidance and feedback. Applies to AI as well.

somewhereoutth• 1 year ago

AI is a [bad] tool. Do not anthropomorphize it, anymore than you would a [particularly ineffective and dangerous] hammer.

micromacrofoot• 1 year ago

I've seen consultants get paid six figures to provide some of the worst advice I've ever heard, so I guess this is progress

bob1029• 1 year ago

I didn’t even know how to think about the changes I needed, because I didn’t understand enough

When you hire a team of consultants, it is typically the case that you are doing so because you have an incomplete view of the problem and are expecting them to fill in the gaps for you.

The problem arises due to the fact that the human consultants can be made to suffer certain penalties if they don't provide reasonable advice. A transformer model ran in-house cannot experience this. You cannot sue yourself for fucking up your own codebase.

andy99• 1 year ago

A lot of that is down to the training, the "voice" is that of a junior Deloitte consultant writing a report. But this was intentional, as in it was trained to have this voice by virtue of the datasets used in the sft and the rlhf goal function.

It would be interesting to see a LLM trained in a completely different way. There's got to be some tradeoff between how coherent the generations are and how interesting they are.

stephc_int13• 1 year ago

I treat LLM based coding assistant as a fast and obedient intern, I give it easy but tedious work like unit tests or documentation or duplicating simple stuff, but I double check everything.

I also use it as a cognitive assistant, I've always found that talking about a design with a colleague helped me to think more clearly and better organize my ideas, even with very little insights from the other side. In this case the assistant is often a bit lacking on the skepticism side but it does not matter too much.

_fat_santa• 1 year ago

I think there's something poetic about that fact that you can go on some AI prompt subreddits and have folks there make posts about turning ChatGPT into an "super business consultant" and then go over hear to read about how it's actually pretty bad at that.

But back on point, I found AI works best when given a full set of guardrails around what it should do. The other day I put it to work generating copy for my website. Typically it will go off the deep end if you try to make it generate entire paragraphs but for small pieces of text (id say up to 3 sentences) it does surprisingly well and because it's outputting such small amounts of text you can quickly make edits to remove places where it made a bad word choice or didn't describe something quite right.

But I would say I only got ChatGPT to do this after uploading 3-4 large documents that outline my product in excruciating detail.

As for coding tasks again it works great when given max guardrails. I had several pages that had strings from an object and I wanted those strings to be put back in the code and taken out of the object. This object has ~500 lines in it so it would have taken all day but I ended up doing it in about an hour by having AI do most of the work and just going in after the fact and verifying. This worked really well but I would caution folks that this was a very very specific use case. I've tried vibe coding once for shits and giggles and I got annoyed and stopped after about 10 minutes, IMHO if you're a developer at the "Senior" level, dealing with AI output is more crumbsome than just writing the damn code yourself.

dgb23• 1 year ago

There are tricks one can use to mitigate some of the pitfalls when using either a conversational LLM or a code assistant.

They emerge from the simple assumptions that:

- LLMs fundamentally pattern match bytes. It's stored bytes + user query = generated bytes.

- We have common biases and instinctively use heuristics. And we are aware of some of them. Like confirmation bias or anthropomorphism.

Some tricks:

1. Ask for alternate solutions or let them reword their answers. Make them generate lists of options.

2. When getting an answer that seems right, query for a counterexample or ask it to make the opposite case. This can sometimes help one to remember that we're really just dealing with clever text generation. In other cases it can create tension (I need to research this more deeply or ask an actual expert). Sometimes it will solidify one of the two, answers.

3. Write in a consistent and simple style when using code assistants. They are the most productive and reliable when used as super-auto-complete. They only see the bytes, they can't reason about what you're trying to achieve and they certainly can't read your mind.

4. Let them summarize previous conversations or a code module from time to time. Correct them and add direction whenever they are "off", either with prompts or by adding comments. They simply needed more bytes to look at to produce the right ones at the end.

5. Try to get wrong solutions. Make them fail from time to time, or ask too much of them. This develops a intuition for when these tools work well and when they don't.

6. This is the most important and reflected in the article: Never ask them to make decisions, for the simple fact that they can't do it. They are fundamentally about _generating information_. Prompt them to provide information in the form of text and code so you can make the decisions. Always use them with this mindset.

benhurmarcel• 1 year ago

Honestly I've already had to work with crappier consultants.

Also, there's a lot of value already in a crappy but fast and cheap consultant.

cafard• 1 year ago

Not that long ago, I noticed https://ploum.net/2024-12-23-julius-en.html on HN.

psadauskas• 1 year ago

I think of AI like a Junior Engineer.

If I understand the problem well enough, and have a really good description of what I want, like I'm explaining it to a junior engineer, then they do an OK job at it.

At my last job, we had a coding "challenge" as part of the interview process, and there was a really good readme that described the problem, the task, and the goal, which we gave the candidate at the start of the session. I copy/pasted that readme into copilot, and it did as good a job as any candidate we'd ever interviewed, and it only took a few minutes.

But whenever there are any unknowns or vagaries in the task, or I'm exploring a new concept, I find the AIs to be more of a hindrance. They can sometimes get something working, but not very well, or the code they generate is misleading or takes me down a blind path.

The thing for me, though, is I find writing a task for a junior engineer to understand to be harder than just doing the task myself. That's not the point of that exercise, though, since my goal is to onboard and teach the engineer how to do it, so they can accomplish it with less hand-holding in the future, and eventually become a productive member of the team. That temporary increase in my work is worth it for the future.

With the AI, though, its not going to learn to be better, I'm not teaching it anything. Every time I want to leverage it, I have to go through the harder tasks of clearly defining the problem and the goal for it.

kyleee• 1 year ago

“ The thing for me, though, is I find writing a task for a junior engineer to understand to be harder than just doing the task myself.”

I have been thinking about this angle a lot lately and realizing how much of a skill it is to write up the components and description of a task thoroughly and accurately. I’m thinking people who struggle with this skill are having a tougher time using LLMs effectively.

rsynnott• 1 year ago

Only really terrible junior engineers will _pretend they know how to do the thing and just produce nonsense_, tho.

insane_dreamer• 1 year ago

This is the key problem. The LLM won't ask questions or clarify something that it doesn't understand; it'll just proceed with what it thinks it knows, and more often than not, get it wrong.

It does usually summarize what you want, but that's simply a restatement of the prompt (sometimes verbatim), which is not the same as the type of follow-up questions that a good Jr engineer would make.

AI Is Like a Crappy Consultant

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