Production reality check

Fix the AI-generated code. Keep the evidence.

The goal is not a more confident prompt. It is a smaller change with a reason, a test and a safe route to users.

01

What happened?

AI can generate a plausible patch without understanding undocumented contracts across the whole system. A repair fails when it treats the visible exception but ignores data shape, permissions, concurrency or deployment configuration.

02

Why this happens

  • The prompt describes the symptom but not the expected contract.
  • A “cleanup” combines the fix with unrelated refactoring and dependency updates.
  • The patch is tested against mocks but not the real service boundary.

03

What you can check yourself

  1. Write a minimal reproduction and identify the first incorrect state.
  2. Read the surrounding code and callers before accepting a replacement.
  3. Inspect the diff for deleted validation, error handling, authorization and migrations.
  4. Add focused regression tests plus one critical end-to-end check.

04

How to test the fix safely

Keep the fix isolated in a branch. Run static checks and tests, deploy it to staging, exercise the original failure and adjacent flows, then release a versioned artifact with rollback available.

05

When you need a developer

Use senior review when the fix affects authentication, money, personal data, migrations, infrastructure or a code path nobody owns. Shipvise can combine AI review with senior review and controlled delivery.

FAQ

Questions people ask after the demo stops working

Can I use AI to fix code written by AI?

Yes, if you constrain the task and verify the result independently. The same model claiming success is not evidence that the defect is fixed.

How small should the patch be?

Small enough that a reviewer can connect each changed line to the reproduced failure. Separate refactoring and upgrades into later changes.

What if there are no tests?

Start with one characterization test around the failing behavior. It gives the repair a measurable target and protects the result from the next change.