Uploaded August 2025 | Updated September 2026, 1 hour ago

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Lakshya A. Agrawal is a Ph.D. student at U.C. Berkeley! Lakshya has lead the research behind GEPA, one of the newest innovations in DSPy and the use of Large Language Models as Optimizers! GEPA makes three key innovations on how exactly we use LLMs to propose prompts for LLMs, (1) Pareto-Optimal Candidate Selection, (2) Reflective Prompt Mutation, and (3) System-Aware Merging. The podcast discusses all of these details further, as well as topics such as Test-Time Training and the LangProBe benchmarks used in the paper! I hope you find the podcast useful!

Links:
GEPA: Reflective Prompt Evolution can Outperform Reinforcement Learning: arxiv.org/pdf/2507.19457
Lakshya A. Agrawal: lakshyaaagrawal.github.io
DSPy 3.0: github.com/stanfordnlp/dspy/releases/tag/3.0.0

Chapters:
0:00 Welcome Lakshya!
4:50 Natural Language Rewards
11:38 Evolution of DSPy
14:55 Pareto-Optimal Candidate Selection
19:58 Domain Specific Knowledge
23:05 Exploration and Diversity
28:35 Test-Time Training
40:10 LangProBe Benchmark
48:05 Prompt Optimization and RL
52:20 Database Tuning with GEPA
1:00:00 Exciting Directions for AI