AI Generated Code Comprehension Issues: Causes and Fixes

AI-generated code is often hard to understand because it is produced without the reasoning that normally comes with it: nobody on the team made the design decisions, the code follows patterns from many different codebases at once, and it tends to be longer and more defensive than it needs to be. The fix is to treat AI output like a pull request from a stranger – review it against a checklist, make the author (the AI) explain itself, and refactor until a teammate could maintain it without the original prompt.

This guide covers the common comprehension problems, why they happen, and a step-by-step way to fix them.

Why AI-generated code is hard to understand

1. The "why" is missing

When a person writes code, the reasoning lives in their head, the ticket and the review discussion. With generated code, the only record of intent is a prompt that's usually lost. Reviewers see what the code does but not why it chose this approach, which makes every later change risky.

2. Plausible but inconsistent style

Models mix conventions: one function uses early returns, the next uses nested ifs; one file handles errors with exceptions, another returns null. Each piece looks reasonable, but together the codebase stops being predictable – and predictability is what makes code easy to read.

3. Too much code

Generated code often adds wrappers, helper functions, configuration options and defensive checks that nothing needs. More lines mean more to read, and unnecessary abstraction hides the real data flow.

4. Hidden assumptions and edge cases

A model fills gaps in the prompt with assumptions – about input formats, time zones, encodings, null values, concurrency. Those assumptions are rarely written down, so the code looks complete while quietly handling only the happy path.

5. Confident naming that doesn't match behaviour

A function called validateUser that also writes to the database, or a variable named total that holds a subtotal. Names generated from the prompt can drift from what the code actually does, and misleading names are worse than vague ones.

6. Unfamiliar or outdated APIs

Models sometimes use library functions that are deprecated, from a different version, or don't exist at all. Readers then have to stop and check documentation for every call.

A review checklist for AI-generated code

Run through this before merging:

Can you explain every line? If not, don't merge it. Ask the AI to explain, or rewrite the part yourself.
Does it match the codebase's conventions? Error handling, naming, logging, folder structure, test style.
Is anything unused or speculative? Delete parameters, options and helpers nothing calls.
Are the names honest? Each function and variable should say exactly what it does or holds.
What happens with bad input? Empty values, very large inputs, unexpected types, network failures.
Do the APIs exist in your versions? Check imports and calls against your lockfile and the docs.
Are there tests that would fail if the logic were wrong? Generated tests sometimes only check that code runs.
Any security issues? Injection, secrets in code, unsafe deserialisation, missing authorisation checks.
Is the intent written down? A short comment or commit message explaining why this approach was chosen.

How to fix code that's already hard to understand

Trace one real input through it. Run the code with a debugger or log statements and follow a single realistic input end to end. You'll see which parts actually matter.
Write characterisation tests first. Before changing anything, capture current behaviour in tests so refactoring doesn't break it.
Delete before you rewrite. Remove dead branches, unused options and needless wrappers. Shorter code is easier to understand.
Rename aggressively. Make names match behaviour; split functions that do two things.
Make assumptions explicit. Turn hidden assumptions into validation, types or a comment.
Unify the style. Apply your formatter and linter, then fix the patterns they can't catch, such as mixed error handling.
Document the decision. One paragraph in the PR or a short comment on why – the thing the AI never gave you.

Prompts that produce more understandable code

You can prevent much of the problem at generation time:

Give context: paste a representative file from your codebase and say "follow these conventions".
Ask for the minimum: "Write the simplest implementation that passes these tests. No extra options or abstractions."
Ask for the reasoning: "Before the code, list the assumptions you're making about the inputs."
Generate smaller pieces: one function at a time is easier to review than a whole module.
Ask for tests that can fail: "Include a test for each edge case you listed."

Should you use an AI code explanation tool?

AI explanations help you get oriented quickly in unfamiliar code, but they can be confidently wrong in the same way generated code can be. Use them as a starting point, then verify by running the code and reading the parts that matter. Manual review remains the step that catches subtle bugs and misleading names.

FAQ

Why is AI-generated code hard to understand?

Because the reasoning behind it isn't recorded, its style is often inconsistent with the rest of the codebase, and it tends to include more code and more hidden assumptions than a human would write.

Is AI-generated code harder to maintain?

It can be, when it's merged without review. Code that has been reviewed, trimmed, renamed and documented is maintained like any other code.

How do I review AI-generated code?

Make sure you can explain every line, check it against your conventions, remove anything unused, verify that the APIs exist, test edge cases and record why the approach was chosen.

What hidden edge cases does AI code miss?

Typical gaps are empty or null inputs, large inputs, time zones and encodings, concurrency, network failures and permission checks.

Can AI explain its own code accurately?

Often, but not always. Treat its explanation as a hypothesis and confirm it by running the code with real inputs.