🦄 Building a Prompt Optimizer
What happens when models can write really good prompts? We dive deep into prompt optimization, exploring JEPA (Genetic Pareto) algorithm, how it works under the hood, and whether you can build your own optimizer. Live demo of a prompt optimizer built with BAML.
Project Details
Open in GitHubBuilding a Prompt Optimizer
What happens when models can write really good prompts? Exploring JEPA, genetic algorithms, and building your own prompt optimizer.
Overview
A deep dive into prompt optimization with special guest Greg from the BAML team. We explore:
- What is JEPA? - Genetic Pareto algorithm for prompt optimization
- How it works - LLM-driven exploration vs traditional gradient descent (GRPO)
- The Pareto frontier - Optimizing across multiple dimensions (accuracy, tokens, latency)
- Genetic algorithms - How prompts "meet and make babies" to explore the search space
- Live demo - Building and running a prompt optimizer with BAML
Key Concepts
- JEPA vs GRPO: JEPA uses LLMs to suggest better prompts instead of fine-tuning with gradients - "the bitter lesson for prompt optimization"
- Pareto optimization: Finding prompts that are optimal across multiple competing metrics
- Avoiding overfitting: When optimizing shared components (system prompts, data models), you need to optimize across all prompts that use them
- Constrained editing: Like Claude Code's Notebook Edit tool, prompt optimizers need constrained ways to edit specific parts of prompts
Links
- Discord Community
- Sign up for the next session on Luma
Whiteboards
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