agentic-eval

Improve AI agent outputs through iterative evaluation by applying structured self-critique loops that review generated content against explicit criteria and refine it until meeting target quality standards.

An evaluator-optimizer pipeline for code refinement separates generation from assessment, running tests to diagnose failures and improve implementations across multiple bounded iterations.

LLM-as-judge comparison works for quality control by evaluating outputs against weighted rubrics, providing structured JSON feedback to measure and guide convergence-aware optimization.

You can use self-critique loops to tighten a draft analysis or report by reviewing the initial answer against clear success criteria and iteratively refining it until reaching the desired quality.

Use rubric-based scoring for AI generation tasks when accuracy and consistency matter, applying it to code, reports, and analysis requiring measurable quality control and test-driven correction.

Limitations of convergence-aware optimization loops include the requirement for clear success criteria and bounded iterations, meaning tasks lacking explicit rubrics or structured feedback may not converge properly.