Reviewing AI-Generated Code: A Skill Worth Practicing

AI writes code fast, but shipping it safely is a human skill. A practical review checklist for engineers using Copilot and other AI tools.

AI assistants now write a meaningful share of the code that lands in production. They autocomplete functions, scaffold endpoints, and draft tests in seconds. What they do not do is take responsibility for the result. That part still belongs to you, and reviewing AI-generated code well is quickly becoming one of the most valuable skills a working engineer can have.

The problem is that suggested code is persuasive. It compiles, it reads cleanly, and it usually looks like something a competent colleague would write. That surface confidence is exactly why it deserves a deliberate second look rather than a quick glance and a merge.

Read It Like a Stranger Wrote It

The most useful mental shift is to treat every suggestion as a pull request from a developer you have never worked with. You would not merge a stranger's code because it looked plausible. You would check whether it actually solves the problem in front of you.

Ask the basic questions you would ask in any review:

• Does it handle the unhappy path? AI loves the sunny case. Empty inputs, network timeouts, and partial failures are frequently missing.
• Are the edge cases real? Off-by-one errors, timezone assumptions, and integer overflow hide easily in confident-looking loops.
• Is this the simplest version? Generated code often adds abstraction or configuration you do not need yet.

Watch for Confident Nonsense

The failure mode unique to AI is the plausible fabrication. A suggestion might call a library method that does not exist, invent a configuration key, or use an API signature from an older version. Because the surrounding code is correct, the one wrong line blends in.

Treat any unfamiliar function, flag, or import as unverified until you have seen it in the official documentation.

Security deserves the same suspicion. Generated code may concatenate strings into SQL queries, log secrets, skip input validation, or pin a dependency to a version with known vulnerabilities. None of this is malicious; the model simply reproduces patterns it has seen, including the bad ones.

Make the Tests Earn Their Keep

When AI writes tests alongside the code, resist the urge to feel covered. Generated tests often assert that the code does what it does rather than what it should do. A test that mirrors a bug will pass happily while the bug ships.

Write at least one test yourself, from the requirement rather than the implementation. If the AI-written code and your independent test disagree, you have learned something valuable before your users did.

Keep Your Own Mental Model

The quiet risk of leaning on assistants is skill erosion. If you accept code you could not have written and cannot fully explain, you are accumulating a debt that comes due during the next outage at 2 a.m. Before merging, you should be able to describe in plain language what the code does and why.

Used well, AI tools are a genuine accelerator. They remove drudgery, surface approaches you had not considered, and let small teams move like larger ones. The engineers who benefit most are not the ones who accept the most suggestions. They are the ones who review fastest and most rigorously, keeping the speed while refusing to outsource their judgment.

The takeaway: let the machine draft, but stay the editor. The keystrokes are cheaper than ever; the responsibility is exactly as expensive as it always was.