agentic-eval

Self-critique loops enable AI agents to evaluate their own outputs against defined criteria and perform iterative improvements. You establish evaluation rubrics and structured JSON parsing to automate quality assessment and refinement.

An evaluator-optimizer pipeline decouples generation from evaluation to enable rigorous quality control and scoring. It separates the AI output generation phase from the assessment phase to facilitate automated quality improvement cycles.

Test-driven refinement automates code generation and validation cycles to ensure functional correctness. It uses iterative evaluation patterns to continuously generate, evaluate, and refine code until it passes defined validation rubrics.

Yes, structured JSON parsing is required to facilitate automated quality assessment within evaluation and refinement loops. Defined evaluation rubrics are also necessary to structure the self-critique and scoring mechanisms.

Iterative refinement patterns should be used when addressing unreliable AI outputs that require high accuracy and compliance. They are ideal for building robust content generation pipelines that must pass strict style and accuracy rubrics.

Self-critique loops rely entirely on the quality of the defined evaluation rubrics and structured JSON parsing. Without clear, accurate criteria, the automated quality assessment and iterative improvement cycles cannot effectively validate outputs.