What AI is good at, and where it slips
AI is fast at boilerplate, explaining unfamiliar code, suggesting approaches and writing first drafts. It slips on exact API details, library versions, edge cases, security and anything that depends on your system. Code that looks right can fail at runtime or open a vulnerability.
The playbook
1. Read it before you run it
If you cannot explain what each part does, you are not ready to ship it. Ask the model to walk through the logic, then check that explanation against the code.
2. Run it and write a test
Execute it with normal inputs, then with edge cases: empty, very large, malformed, unexpected types. Add a test that would have failed if the code were wrong.
3. Verify the APIs and versions
Check every library call against the official documentation for the version you use. Models can invent functions or use removed ones (see knowledge cutoff). Check that every dependency exists and is the one you intend, and that the name is not a typo-squat.
4. Look at security explicitly
Check input handling, authentication, secrets, injection, file and network access. Ask a second model to review the code for vulnerabilities and compare with your own reading.
5. Compare approaches across models
Send the same task to several models. Where the approaches agree, you have a conventional solution. Where they differ (different algorithm, different library, different trade-off), read both: the difference is often a real design decision you should make yourself.
6. Check the cost of being wrong
A script you run once is low stakes. Code that handles payments, personal data, infrastructure or other people's systems deserves human review, a staging run and a rollback plan.
What to hand to a human
Security-sensitive code, schema and data migrations, anything touching production data, architecture choices and the final approval to merge.
Try it on your own question
Run the question you care about through six independent models and read where they agree and where they split. Ask 6 AIs on Satcove, free, no card required.
Frequently asked questions
Is AI code reliable if it runs? Not always. Running without an error does not mean correct for all inputs, safe or maintainable. Test edge cases and review it.
Why do models invent functions? They generate plausible code from patterns, so they can produce calls that look right but do not exist in your version of the library.
Is it useful to ask several models for code? Yes. Agreement shows the conventional solution, and differences point to a design choice or a possible error to examine.