agent-quality-patterns

Evaluating AI agent quality requires implementing self-critique loops and LLM-as-judge scoring to measure correctness. This establishes a robust evaluation process, ensuring reliability before deployment.

The LLM-as-judge pattern uses a separate LLM to consistently evaluate and score output quality. It acts as an automated evaluator within an evaluator-optimizer pipeline to refine AI accuracy.

Setting up golden dataset evaluation involves testing your AI agent against a curated set of inputs. This validates agent correctness and ensures production-ready performance across known scenarios.

A self-critique loop improves LLM output quality by having the agent evaluate its own responses and refine them iteratively. This process catches errors and enhances accuracy before final output.

The best way to measure correctness of LLM-powered features is combining evaluator-optimizer pipelines with golden dataset evals. This provides measurable, consistent scoring to bridge the gap between prototype and production.

You can use self-critique loops for AI agent prototypes to enhance output quality. This technique fits any LLM-powered feature needing refinement, though it requires an evaluator mechanism to function effectively.