How to Read AI-Generated Code Before It Reaches Production

AI assistants now write the first draft of a large share of code. Writing keeps getting cheaper. What stays expensive is understanding a change well enough to catch the edge case the model missed. Most engineers were trained to write code, not to read unfamiliar code, so they open a file and scroll from top to bottom like a book. A few minutes later they still cannot say what it does.

Code is not a story. It is a graph of who calls what. Reading it well is a separate skill, and it is now one of the most valuable in a web team.

Start at the entry point and read the tests first

Begin where the outside world enters the system: the route handler, the server action, the queue consumer. Do not open folders for the sake of it. Find the line that receives the request, then follow the calls outward.

Before opening the implementation, read the test for the happy path. A short test states the contract: these inputs go in, this response must come out. It is not the whole story, but it gives a reference point before any source is read.

Then follow the data rather than the functions. Pick the variable that matters and watch its journey: where it is created, how it is checked, how it is transformed, what is finally returned. In an authentication flow that means the user record, the comparison of the submitted password against a stored hash, the signed token with its expiry, and the response. Read this way, the core flow becomes clear in well under a minute.

Skip on the first pass, then probe one failure path

Helpers, rate limiters, audit logging and validators can be skipped at first, unless they change the request, block the flow or explain the bug being investigated. Middleware often decides whether a handler runs at all, so the skip is temporary. Map the shape first. A common mistake is feeling obliged to open every helper the moment it appears, which burns time without adding understanding.

Once the happy path is clear, read exactly one failure path. The happy path shows what the code does. The failure path shows what it gets wrong. In a login example, two questions are enough to expose real weaknesses:

• Does the error message differ when the email exists compared with when it does not?
• Does rejecting a wrong password take longer than rejecting an unknown account?

Either difference lets an attacker work out which accounts are real, even if the other one looks identical. These are exactly the edge cases that generated code tends to leave out, because nothing in a happy-path test would ever fail.

Write the trace down in one sentence

The last habit is the one almost nobody practises. Summarise what you read in a single sentence in your own notes. For the login flow it might be: find the user by email, check the password against the stored hash, sign a token with the user ID, return it. If that sentence cannot be written, the code was looked at but not understood.

What this means for web development practice

When a model drafts most of a feature, the reviewer is the last person who actually understands it. That makes reading discipline part of the delivery process, not a personal preference. Entry point first, tests as the contract, data followed end to end, one failure path questioned, one sentence to prove comprehension: this is a repeatable routine that any senior engineer can apply to a pull request in a few minutes.

For product teams deciding how to build, the takeaway is practical. Fast generation is only valuable when someone on the team can explain what was generated. At Curiosive, that is why senior engineers read every line before it ships.