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UID:pretalx-devconf-us-2026-3FHZHW@pretalx.devconf.info
DTSTART;TZID=EST:20260925T090000
DTEND;TZID=EST:20260925T102000
DESCRIPTION:Prompt optimization is still mostly hand-crafted: edit the word
 ing\, rerun a few examples\, inspect failures\, and repeat. That is like h
 and-crafting image kernels for every new vision problem. It can work for s
 imple cases\, but it does not scale well\, and it is hard to reproduce.\n\
 nGEPA offers an algorithmic alternative. It is a gradient-free form of RL 
 for LLM systems: sample candidate behaviors\, evaluate them\, use feedback
  from failures\, and search for better policies. But instead of updating m
 odel weights with PPO or GRPO\, GEPA optimizes the text artifacts that sha
 pe model behavior: prompts\, and system instructions.\n\nThis talk is for 
 engineers building LLM applications or agent workflows who want to move fr
 om prompt tweaking to evaluation-driven optimization. We’ll walk through
  the core GEPA recipe: writing an evaluation callable\, returning both rew
 ard and natural-language feedback\, exposing one or more text variables to
  optimize\, and running the optimizer on a small dataset. \n\nAttendees wi
 ll leave with a runnable notebook\, a mental model for when GEPA is useful
 \, and a reusable recipe for applying automatic prompt optimization to the
 ir own LLM systems.
DTSTAMP:20260727T174808Z
LOCATION:107 (Capacity 20)
SUMMARY:The Cheapest Way to RL tune LLMs: Getting Started with GEPA - Rohan
  Awhad
URL:https://pretalx.devconf.info/devconf-us-2026/talk/3FHZHW/
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