Prompts, RAG, LLM tuning, Harness… What’s next?

▤ Summary

This piece explores how to build and optimize harnesses for compound AI systems, especially around LLMs and RAG pipelines. It compares prompt optimization methods such as OPRO, TextGrad, and GEPA, and it shows how meta-harnesses can improve retrieval, reasoning, and tool-use workflows. It also describes a virtual DBA setup that uses MCP tools, record/replay, and PostgreSQL evidence to evaluate and tune agent behavior. The article highlights measurable gains from optimization loops and emphasizes Pareto-based selection, stage composition, and evidence-driven debugging. Overall, it focuses on engineering methods for making AI workflows more reliable and performant.

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