Using AI in an Engineering Workflow Without Losing Quality
How to use AI for requirement analysis, test cases, code review, and documentation without trusting it blindly.
Where should AI fit?
AI is most useful in steps that need drafts, questions, and risk checks. I use AI before coding to clarify requirements, during coding to explain errors, and after coding to create review checklists.
A simple review prompt
A useful prompt should include enough context:
This is a change in an ASP.NET Core API.
Review it for validation, authorization, database transactions, error handling, and test cases.
Only list issues with real risk and suggest how to verify them.
Never skip verification
AI can sound confident and still be wrong. The final result must go through builds, tests, code reading, and a human explanation of the logic. If you cannot explain it, it is not ready to merge.
Practical benefits
Used well, AI saves time on repetitive work: creating test checklists, drafting documentation, comparing solution options, and finding edge cases. The final decision still belongs to the engineer writing the code.