agentic-eval

Self-critique loops for AI code generation are implemented by having agents analyze their own outputs against evaluation rubrics and iteratively refine the code based on structured critique results.

An evaluator-optimizer pipeline for iterative refinement is a system architecture where distinct components generate, evaluate, and refine AI outputs through structured self-critique to meet specific quality standards.

Test-driven code refinement evaluates AI-generated code by running it against provided tests, automatically analyzing failures, and iteratively fixing the code based on the self-critique of test results.

Yes, self-critique systems require structured output formats to reliably parse critique results and evaluation scores generated by the AI during the iterative reflection and refinement process.

Yes, you can use rubric-based evaluation to improve AI agent outputs by having the AI draft content, critique it against a defined rubric, and revise it to ensure accuracy and clarity.

Self-critique for AI quality improvement relies heavily on the agent's ability to accurately evaluate its own outputs and requires structured output parsing, limiting its effectiveness with unstructured or highly subjective content.