- 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."
- 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.
- 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...
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- So this is the modern day equivalent of an O'Reilly book with the title "Mastering LLMs"?
- Looks fantastic, thanks for the deep dive on structured output. Will read thoroughly.
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- Fantastic!