CHEAPUNO guide

How to check if your AI code reviewer is independent from the generator

Identify bias gaps when the same model family writes and reviews code by pointing a different AI at your PRs to catch blind spots.

CHEAPUNO EditorialSources checked 10 10 月, 2026

What you are probably seeing

You notice that code generated by your primary AI tool passes review without raising critical logic errors or edge case failures.

Your deployment metrics show a steady increase in subtle bugs or security vulnerabilities that only surface after the code is in production.

Why it happens

When the same model family generates and reviews code, it shares underlying patterns and biases, making it less likely to challenge its own output.

The model may interpret ambiguous requirements in the same way during generation and review, leading to a consistent but incorrect understanding of the task.

What changed recently

Step by step

1. Identify a secondary AI model from a different vendor or architecture to act as your independent code reviewer.

2. Configure your CI/CD pipeline to run the secondary model on all pull requests before merging to the main branch.

3. Create a review checklist that specifically asks the secondary model to challenge assumptions and edge cases in the generated code.

4. Log instances where the secondary model flags issues that the primary model missed to track the value of independent review.

5. Review the flagged issues weekly to determine if the secondary model provides actionable feedback or if its prompts need adjustment.

Ways to go further

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