Vanishing Gradients

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.
Ask episode
AI Snips
Chapters
Transcript
Episode notes
ADVICE

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.
INSIGHT

Evals are Essential

  • Evaluations (evals) are crucial for building LLM-powered applications.
  • Without measuring and tracking progress, improvement is impossible.
ADVICE

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.
Get the Snipd Podcast app to discover more snips from this episode
Get the app