Pin Your AI Review Configuration Like a Dependency

Описание и применение

Пинните конфигурацию AI‑ревью как зависимость: храните модель, шаблон, параметры и контекст в репозитории, чтобы каждый запуск можно было воспроизвести и проверить, а результаты сохранять в CI для аудита.

Задача

AI-assisted code reviews lose trust when the model, prompt, and diff cannot be reproduced, making it hard to audit or compare results across model updates.

Результат

By pinning AI review configurations, teams gain an audit trail, can detect regressions caused by model updates, and make informed merge decisions based on reproducible evidence.

Рабочий процесс

• 1. Create a pin file (JSON) with model, prompt_version, temperature, max_tokens, and rules.
• 2. Store the pin file alongside the code in the repo.
• 3. Run the review harness (e.g., review_harness.sh) passing the diff and pin file.
• 4. Capture the review output artifact (JSON) and fingerprint.
• 5. Store the artifact in CI for audit and future comparison.
• 6. Re-run the harness after model or prompt changes to compare fingerprints and outputs.
• 7. Use the decision table to handle review comments based on reproducibility.

Доказательства

AI-assisted code review only earns trust when the exact model, prompt, and diff can be replayed from a stored configuration.

Источник

Store the configuration in the repository so the review is tied to the code it evaluated, not to a developer's memory.

Источник