Mesrai
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Use case · AI-Generated Code

Review AI-written code before it reviews you.

Coding assistants let every developer produce far more code than anyone can carefully read. Mesrai reviews that code against your repository and your rules, catching the specific ways AI-generated changes go wrong.

Rule sync
.cursorrules, CLAUDE.md
Context
Whole repository
Also runs
Pre-push via CLI
Start free See examples

How teams using AI coding assistants use AI code review

Teams use AI code review to keep up with code written by assistants such as Cursor, GitHub Copilot and Claude Code. Mesrai reviews each pull request against the actual repository, catching the failure modes typical of AI-generated code — calls to functions that don't exist, logic that duplicates an existing helper, missing authorization or error handling, and ignored team conventions — and it can enforce the same rule files the assistants use.

I.

Why AI-written code needs a different review

  • 01

    Pull requests got bigger and more frequent; human reviewers skim.

  • 02

    AI code looks plausible: it compiles, it's well formatted, and it can still call an API that doesn't exist or skip an authorization check.

  • 03

    Assistants don't know the helper in another folder, so they rewrite it — with slightly different behaviour.

  • 04

    Each assistant has its own rule file, and they drift apart.

II.

How Mesrai reviews AI-generated changes

Checked against the real repository

The repository graph verifies that called functions, types and endpoints exist and are used correctly elsewhere.

Duplicate logic detection

Points out when new code re-implements an existing helper, and links to it.

One rule set for humans and assistants

Mesrai can import rules from .cursorrules, CLAUDE.md and similar files, and IDE Rule Sync keeps assistants and review on the same conventions.

Review before the PR

Run the Mesrai CLI before pushing to catch issues in assistant output while it's still on your machine.

III.

Real-world scenarios

3 examples

Illustrative examples of the kind of change teams using AI coding assistants make every week, and the review comment Mesrai would post.

Scenario 01

An invented SDK method

An assistant writes a file-upload handler using a storage SDK method that sounds right.

src/uploads/save.ts
await storage.bucket(BUCKET).uploadSecure(file, { private: true });
MesraiBug agentMUST_FIX

The storage client in this repo has no uploadSecure method — the existing uploads in src/uploads use bucket.file(name).save(data, { private: true }). This will throw at runtime; the tests mock storage, so they won't catch it.

Result: A runtime failure hidden by mocked tests is caught in review.

Scenario 02

A second, slightly different helper

Asked to 'add slug generation', the assistant writes a new slugify function.

MesraiMesrai RulesSUGGESTION

src/lib/strings.ts already exports slugify(), which also handles Unicode and de-duplication. This new version would produce different slugs for the same titles. Reuse the existing helper.

Result: The codebase keeps one behaviour for slugs instead of two that disagree.

Scenario 03

Generated endpoint, missing authorization

A generated CRUD endpoint updates a project by id after checking only that the user is logged in.

MesraiSecurity agentMUST_FIX

The update handler doesn't check that the user belongs to the project's organization, unlike the other project routes, which call assertProjectMember(). Add the same check here.

Result: The convention the assistant didn't know is enforced anyway.

IV.

Set up for assistant-heavy teams

Keep one source of truth for conventions, and review both before and after the push.

  1. 1

    Turn on rule file detection so Mesrai imports .cursorrules, CLAUDE.md and similar files.

  2. 2

    Add rules for your recurring AI mistakes — e.g. 'use existing helpers from src/lib before writing new ones'.

  3. 3

    Install the Mesrai CLI and run it as a pre-push check.

  4. 4

    Lower the suggestion limit on very large pull requests and ask for smaller ones.

V.

Who uses it, and how

Developers using assistants
Get a check on generated code before teammates see it.
Reviewers
Focus on whether the change is right, not whether every call exists.
Tech leads
Keep one set of conventions across assistants and review.
VI.

What to measure in Pulse

PR size

AI makes pull requests bigger; keep them reviewable.

Change failure rate

Check that more output isn't producing more incidents.

VII.

What teams achieve

Fewer runtime failures from invented or misused APIs

Less duplicated logic in the codebase

Conventions applied to generated code

Reviewers able to keep up with AI-era volume

VIII.

AI-Generated Code — frequently asked

3 questions

  • Can AI review AI-generated code effectively?

    +

    Yes, when the reviewer has context the generator lacked. Mesrai checks generated code against the actual repository, your rules and the linked ticket, which catches mistakes the assistant couldn't see.

  • Which coding assistants does this work with?

    +

    Any of them. Mesrai reviews the pull request, regardless of how the code was written, and can import rules from Cursor, Claude Code and other assistant rule files.

  • What are the most common problems in AI-generated code?

    +

    Calls to non-existent functions or wrong SDK signatures, duplicated helpers, missing authorization and validation, swallowed errors, and ignoring team conventions.

Further reading

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