

Episode 29: Lessons from a Year of Building with LLMs (Part 1)
20 snips Jun 26, 2024
Experts from Amazon, Hex, Modal, Parlance Labs, and UC Berkeley share lessons learned from working with Large Language Models. They discuss the importance of evaluation and monitoring in LLM applications, data literacy in AI, the fine-tuning dilemma, real-world insights, and the evolving roles of data scientists and AI engineers.
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Iterative Evaluation
- Evaluate LLM output quality throughout the development lifecycle, not just at the end.
- This iterative approach allows for continuous improvement and refinement, like training an ML model.
Evals are Essential
- Evaluations (evals) are crucial for building LLM-powered applications.
- Without measuring and tracking progress, improvement is impossible.
Prioritize Process over Tools
- Focus on learning the process of AI development, not just the tools.
- Avoid fixating on specific technologies and prioritize understanding the underlying principles.