GEPA Agent Optimizer with Lakshya A. Agrawal [podcast]
Lakshya is a Ph.D. student at U.C. Berkeley, where he has lead the research behind GEPA: Reflective Prompt Evolution can Outperform Reinforcement Learning!
GEPA is a huge step forward for automated prompt optimization, DSPy, and the broader scope of integrating LLMs with optimization algorithms!
The podcast discusses all sorts of aspect of GEPA from the Reflective Prompt Mutation to Pareto-Optimal Candidate Selection, Test-Time Training, the LangProBe Benchmark, and more!
I learned so much from discussing these things with Lakshya, and I really hope you enjoy the podcast!
YouTube: https://www.youtube.com/watch?v=fREQrxhBSk0
Spotify: https://creators.spotify.com/pod/profile/weaviate/episodes/GEPA-with-Lakshya-A--Agrawal---Weaviate-Podcast-127-e36qaq1/a-ac3oens