agentic-eval

Refine AI outputs using LLM-as-judge by implementing self-critique loops where agents evaluate responses against defined criteria. This iterative process identifies accuracy and clarity shortcomings, enabling agents to revise generated content for quality assurance.

An evaluator-optimizer pipeline separates generation and evaluation processes for clearer workflow management. It enables targeted refinement by allowing an independent evaluator to critique generated outputs before an optimizer applies revisions.

Yes, you can use test-driven code refinement to fix AI generated code. This approach utilizes unit tests to drive code generation and bug fixing, ensuring the final output passes defined quality and functional benchmarks.

Set up rubric-based evaluation systems by defining specific accuracy, clarity, and completeness criteria for AI generated reports. Agents first produce a draft, critically assess it against the rubric, and revise based on identified shortcomings.

The limitation of self-critique for quality-critical generation is that AI agents may lack external ground truth, potentially reinforcing their own biases during evaluation. Complex rubric-based evaluation systems require predefined criteria to mitigate missing accuracy gaps.