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Automate iterative code quality improvements by mutating a single target file, running a stable evaluator command, and committing or reverting changes based on whether the parsed score improves.

Automated experimentation loops support prompt engineering optimization by iteratively mutating prompt templates, evaluating outputs against a fixed scoring harness, and rolling back changes that fail to improve the score.

Autonomous iterative improvement for configuration tuning works by applying mutations to a single target file, evaluating results with an immutable scoring command, and automatically committing improvements while reverting regressions.

Automated code evaluation requires a single mutable target file for applying changes and an immutable evaluation harness to preserve score comparability across all iterations.

Set up strategy optimization by defining a target file, an evaluator command returning a numeric score, and a parse rule; the loop commits accepted changes to git and rolls back regressions automatically.

Limitations include restricting mutations to a single target file per experiment and requiring a stable evaluator command that never changes, ensuring changes remain traceable and scores comparable.