AI cannot take responsibility for a change
A generated answer can be fluent and still be wrong about your requirements, architecture, or deployment environment. The engineer who merges the change remains responsible for its behavior.
If you cannot explain what a proposed change does, why it is needed, and how you will verify it, do not accept it yet. Ask for an explanation, reduce the scope, or investigate the code directly.
Be cautious with security-sensitive decisions
Authentication, authorization, cryptography, payment handling, and privacy controls depend on precise assumptions. A generic code sample may omit threat modeling, framework protections, or operational constraints.
Use authoritative project documentation and established review processes. Ask AI to explain a known design or enumerate questions for a reviewer, but do not make it the sole basis for a security decision.
Verify APIs, packages, and version details
Models may suggest APIs that existed in another version or a package that is unfamiliar or unsuitable. Before relying on a suggestion, check the installed version, official documentation, maintenance status, and the project’s dependency policy.
Do not install a dependency just because generated code imports it. Confirm the need and inspect the package source and provenance according to your team’s process.
Gather evidence before debugging by guesswork
If you do not have a reliable reproduction or know which component failed, first collect the error, request data, relevant logs, and recent changes. Otherwise, a model may confidently optimize the wrong hypothesis.
When the evidence is incomplete, ask for diagnostic steps rather than a patch. Reproduce the failure and change one thing at a time so you can identify cause and effect.
Protect confidential information
External services have different data handling terms and controls. Do not paste credentials, personal information, private customer records, or proprietary code unless the specific tool is approved for that data and your organization permits it.
When unsure, remove identifying details, create a minimal example, or use an approved local or enterprise tool. A sanitized reproduction is often enough to explain a technical issue.
A practical decision checklist
If key answers are no, pause and gather context or consult the appropriate person. Knowing when not to use AI is part of responsible engineering practice.
- Can I describe the task and expected behavior clearly?
- Is the information I plan to share permitted for this tool?
- Can I independently verify the suggested API and approach?
- Do I understand the change well enough to maintain it?
- Can tests or another repeatable check establish that it works?
- Does this decision need a security, privacy, or domain expert?
Keep learning
Explore the AI-Powered Developer Productivity course or read the free chapter preview.