Prompt optimization is still mostly hand-crafted: edit the wording, rerun a few examples, inspect failures, and repeat. That is like hand-crafting image kernels for every new vision problem. It can work for simple cases, but it does not scale well, and it is hard to reproduce.

GEPA 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 model weights with PPO or GRPO, GEPA optimizes the text artifacts that shape model behavior: prompts, and system instructions.

This talk is for engineers building LLM applications or agent workflows who want to move from prompt tweaking to evaluation-driven optimization. We’ll walk through the core GEPA recipe: writing an evaluation callable, returning both reward and natural-language feedback, exposing one or more text variables to optimize, and running the optimizer on a small dataset.

Attendees will leave with a runnable notebook, a mental model for when GEPA is useful, and a reusable recipe for applying automatic prompt optimization to their own LLM systems.