agentic-eval

Iterative refinement improves code quality by having the AI generate code, create tests for that code, and then automatically fix any test failures based on its own critique. This evaluator-optimizer pipeline ensures generated code meets specifications before delivery.

An evaluator-optimizer pipeline uses the LLM-as-judge pattern to assess generated code against rubric-based criteria, identify flaws, and apply corrections iteratively. This approach integrates testing frameworks to validate outputs and drive refinement until quality standards are satisfied.

Yes, rubric-based evaluation systems define specific quality criteria that the agent uses to self-critique its output. When combined with test-driven code refinement, the agent writes code, generates tests, evaluates results against the rubric, and fixes failures iteratively.

Yes, agentic-eval operates with no external dependencies, providing self-contained patterns and techniques for measuring and improving agent response quality. It supplies the evaluation framework and refinement logic directly through its scripts and reference components.

Use self-critique and iterative refinement when AI-generated outputs must meet specific quality standards, such as complex code generation requiring test validation. It is essential when initial responses need automated evaluation and correction to achieve acceptable quality.