Episode

GEPA with Lakshya A. Agrawal - Weaviate Podcast #127!

Podcast
Weaviate Podcast
Published
Aug 13, 2025
Duration seconds
3715
Processing state
not_requested
Canonical source
https://podcasters.spotify.com/pod/show/weaviate/episodes/GEPA-with-Lakshya-A--Agrawal---Weaviate-Podcast-127-e36qaq1
Audio
https://anchor.fm/s/cffc3468/podcast/play/106817793/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-7-13%2F3a8297e4-ba0e-05fa-c8b9-69fbc33d37df.mp3
JSON
/v1/public/podcasts/weaviate-podcast-6288219/episodes/gepa-with-lakshya-a-agrawal-weaviate-podcast-127
Markdown
/podcast/weaviate-podcast-6288219/gepa-with-lakshya-a-agrawal-weaviate-podcast-127.md

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Summary

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!