Taming LLMs – A Practical Guide to LLM Pitfalls with Open Source Software

https://www.souzatharsis.com/tamingLLMs/markdown/toc.html

by sebg • 2 years ago

168 30 2 years ago

souzatharsis.com

msp262• 2 years ago

Looks fantastic, thanks for the deep dive on structured output. Will read thoroughly.

msp262• 2 years ago

Actually having read it now, there's way too much fluff. This could be way shorter.

simonw• 2 years ago

It feels a little risk to me to construct a book like this with so many LangChain examples - my impression of LangChain is that it's still a project with a quick rate of development that might not stay stable, though maybe I'm wrong about that.

zbyforgotp• 2 years ago

LangChain is great for finding out who has a clue.

pkkkzip• 2 years ago

i think this is an excellent litmus test. Anyone caught pushing langchain signals pedantry and should be completely ignored.

factormeta• 2 years ago

From a developer with extensive database ETL experience, is it still necceary to learn or use LangChain? Would it be just easier to load directly to vector db?

WesleyJohnson• 2 years ago

How is this helpful? Not everyone can keep up with the rapidly shifting world of AI; it's worse than JavaScript. People have day jobs that don't revolve around AI and don't have the bandwidth to devote endless hours trying to keep up, while still getting work done that makes money for the business they're in.

I read through the article and it provided a lot of helpful information on the pitfalls with LLMs which, based on the title, is its intended purpose. I didn't take it as a shining recommendation for LangChain. If your point is that suggesting LangChain means they're not keeping up, as someone else stated, and because of that the other information is probably dated as well - that's far more helpful.

I wouldn't mind seeing a current guide on pitfalls and practical examples of interacting with LLMs with raw python - from someone who "has a clue".

knowsuchagency• 2 years ago

Agreed. I wrote my own LLM abstraction library in a few hundred lines of code: https://github.com/knowsuchagency/promptic

parmesean• 2 years ago

Fantastic!

constantinum• 2 years ago

The chapter where there is a comparison of techniques for structured data extraction is insightful.[1] Does anyone wants to explore more on the structured data extraction techniques, do refer to this piece [2]

[1] https://www.souzatharsis.com/tamingLLMs/notebooks/structured...

[2] https://unstract.com/blog/comparing-approaches-for-using-llm...

agnishom• 2 years ago

So this is the modern day equivalent of an O'Reilly book with the title "Mastering LLMs"?

huqedato• 2 years ago

I took a brief look into this guide. What surprises me is that it sounds like being generated with AI. Am I the only one who thinks so?

Just read this paragraph: "In conclusion, while managing output size limitations in LLMs presents significant challenges, it also drives innovation in application design and optimization strategies. By implementing techniques such as context chunking, efficient prompt templates, and graceful fallbacks, developers can mitigate these limitations and enhance the performance and cost-effectiveness of their applications. As the technology evolves, advancements in contextual awareness, token efficiency, and memory management will further empower developers to build more robust and scalable LLM-powered systems. It is crucial to stay informed about these developments and continuously adapt to leverage the full potential of LLMs while addressing their inherent constraints."

tarboreus• 2 years ago

LLMs love to sum things. up. On the one hand, this. On the other hand, that! It is very important. Challenges!

Taming LLMs – A Practical Guide to LLM Pitfalls with Open Source Software

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