Episode 30: Lessons from a Year of Building with LLMs (Part 2)
Vanishing Gradients
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Lessons Learned from Working with Large Language Models
The chapter delves into various lessons learned from working with Large Language Models (LLMs), highlighting the importance of optimizing prompts for model performance and avoiding unnecessary information overload. Discussions include debugging systems with large prompts, exploring the complexities of SQL schemas, and the impact of carefully examining prompts when using sources like RAG. The speakers also share insights on developing domain-specific query languages, improving data quality through LLM critiques, and the continuous process of refining models using methodologies and synthetic data.
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