Featured writing
When to read AI-generated code
AI-assisted programming made one review question oddly polarizing: should you read all the generated code?
The absolute answers are easy and mostly unhelpful. Reading every line sounds responsible until the team is reviewing harmless boilerplate for the third time that week. Trusting the tool sounds efficient until generated code touches security, money, data integrity, or a path nobody understands.
The useful rule is situational: read the code deeply when the code is where the risk lives.
This article gives you a simple way to decide when generated code needs a line-by-line review, when tests and traces should carry more of the review, and when a quick scan is enough.
Start with ownership
The author owns AI-generated code exactly like any other code. The tool can draft, refactor, explain, and test, but it cannot hold accountability for production behavior. The engineer submitting the change owns the intent, the tradeoffs, and the failure modes.
The engineer does not need to inspect every generated token with the same intensity. The engineer does need to understand enough to defend the change and operate it when something breaks.
Read for risk, not guilt
The decision should come from risk, not guilt about using AI.
- Low risk
Generated glue, small copy changes, simple types, or repetitive code with strong tests.
- Medium risk
Application logic, data transformations, state changes, or code that affects a real workflow.
- High risk
Security, permissions, billing, migrations, concurrency, privacy, or unfamiliar architecture.
- Unknown risk
Code you cannot explain yet. Treat it as high risk until the shape is clear.
Low-risk code can often be reviewed through intent, tests, and a quick scan. Medium-risk code deserves selective reading around the logic that matters. High-risk or unknown-risk code deserves a real line-by-line pass.
Use tests to choose where your eyes go
Good tests do not replace understanding. They point your attention toward the behavior that matters.
If generated code changes a formatter and the unit tests cover the edge cases, you probably do not need to read every branch with the same intensity. If generated code changes authorization logic, tests are only the start. You read the implementation because the implementation is the risk surface.
The useful review question is "What would hurt if this were wrong?" Who wrote the first draft matters less than the blast radius of a bad answer.
A practical review rule
Use this rule when reviewing your own AI-assisted work or someone else's:
| Situation | Review style |
|---|---|
| Boilerplate with narrow behavior | Scan the diff, verify naming and integration, rely on focused tests. |
| Business logic | Read the main path and edge cases, then check tests against the intended behavior. |
| Security, money, privacy, migrations, concurrency | Read line by line and ask for a clear rationale. |
| Code nobody on the team can explain | Stop and simplify, document, or rewrite until someone can own it. |
This keeps the standard high without pretending every generated line carries the same weight.
What managers should expect
Managers should not ask engineers to perform fake diligence. "Read everything" is easy to say and hard to verify. A better standard is explainability:
- Can the author explain what changed?
- Can they name the risky paths?
- Can they show the tests or evals that cover the expected behavior?
- Can they say what they inspected manually and why?
- Can the reviewer follow the rationale without trusting the model?
That standard respects speed and accountability at the same time.
The useful middle
Read the code when the generated implementation is the product risk. Read the tests, traces, and behavior first when they tell you more than a line-by-line pass over safe scaffolding.
The line I want from an engineer is "I know where the risk is, and I reviewed that part deeply enough to own it."