codex-evaluation

An eval-driven improvement loop automates iterative quality enhancement for Codex tasks by combining deterministic scoring with LLM-as-judge evaluation. You run a baseline evaluation, log initial scores to iteration_log.md, and apply structured multi-dimension scoring to track progress.

LLM-as-judge evaluation uses a language model to assess prompt outputs across multiple dimensions, providing structured scoring alongside deterministic metrics. This combination enables reproducible quality measurement and continuous improvement for Codex-guided tasks.

Yes, deterministic scoring combined with LLM-based evaluation supports debugging, optimization, and validation scenarios within Codex-driven projects. The structured workflow applies guardrails and convergence criteria to ensure safe, reproducible improvements across iterative debugging cycles.

The best way to measure progress is using a structured eval-driven loop with baseline evaluation, multi-dimension scoring, and iteration logging. This produces concrete improvement records and applies convergence criteria to determine when optimization reaches acceptable quality thresholds.

Yes, guardrails are required for safe, reproducible improvements in automated evaluation loops. The workflow enforces convergence criteria and iteration logging to prevent unbounded changes, ensuring that each scoring iteration produces validated, measurable quality enhancements.