If you've ever thought "I wish I could improve my LLM pipeline without burning GPU budget on...
If you've ever thought "I wish I could improve my LLM pipeline without burning GPU budget on fine-tuning," GEPA is what you've been waiting for.
TL;DR
The Problem
Picture this: you've built a multi-step RAG pipeline — retrieve → reason → format. The retriever is good, but the final answer quality is mediocre. You want to improve it.
Your options:
Moving beyond the slow, costly trial-and-error of RL, GEPA teaches AI systems to learn and improve using natural language.
TL;DR: Running LLM evaluations on every PR will burn your GPU budget faster than you can blink. We...
In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve how a small language model solves multi-step…
I run a lot of RAG pipelines. And for a while, I was doing what most people do: retrieve the top-k...
You deployed a RAG chatbot. The answers are vague. You bump the LLM from GPT-3.5 to GPT-4. The...
Key Takeaways Use RAG for knowledge retrieval, changing data, and rapid iteration. Use fine-tuning...
