Pin Your AI Review Configuration Like a Dependency

说明与用途

将 AI 评审配置像依赖一样固定：将模型、提示、采样参数和差异上下文存储在仓库中，使每次评审可重现，并在 CI 中保存结果以供审计。

问题

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.

结果

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.

工作流

• 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.

证据

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

来源

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

来源