Hill-Climb Your Agent Prompts with a Frontier Model, Then Verify on Your Own Evals
JensHonack · x · 2026-11-11
A practical, high-ROI method for improving agent systems: use a frontier model to refine your context/prompt engineering.
- Point a frontier model at your decisions and have it hill-climb your prompts and questions iteratively
- Validate on your own evals: check classification consistency (best of 5) and improvements in F1/recall/precision
- The author stresses this is mandatory if you're using a smaller model as a reward function in RL
Core idea: use a stronger model as an optimizer to tune the prompts of weaker models — cheap but effective.
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