CHEAPUNO guide

How to verify AI code before trusting it

Stop blindly accepting AI generated code by adopting a manual review and testing protocol that catches logical errors.

CHEAPUNO EditorialSources checked 8 10 月, 2026

What you are probably seeing

You see production errors that do not match the AI generated test results, indicating the logic is flawed despite passing automated checks.

You spend more time debugging AI output than writing code from scratch because you cannot trace the specific cause of failure in the generated structure.

Why it happens

AI models predict the next most likely token based on patterns, not actual execution logic, so they can create code that looks correct but fails in specific edge cases.

Relying on automated tests alone is insufficient because AI often generates tests that mirror its own flawed assumptions, creating a feedback loop where errors remain undetected.

What changed recently

Step by step

1. Manually trace the execution path of critical functions by hand before running them to spot logical gaps.

2. Write specific negative test cases that attempt to break the code using invalid inputs or edge scenarios.

3. Isolate AI generated snippets into small, reviewable chunks rather than accepting large blocks of code at once.

4. Log every assumption the AI made about your system architecture and verify each one against your actual codebase.

5. Perform a peer review where a human who was not involved in the generation process audits the final implementation.

Ways to go further

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