AI Coding Assistants

AI Coding Assistant for Pull Request Review

Most teams use AI coding assistants to write code, but they rarely use them to review it. A first-pass AI review catches obvious issues, standardizes feedback, and leaves humans to judge architecture and intent. The result is fewer missed nits, faster merges, and reviewers who actually spend time on design instead of hunting for missing tests.

FreeLast tested: 2026-08-26Audience: Developers, engineering leads

Why first-pass AI review works

Human review is bottlenecked by attention, not skill. Reviewers miss simple things when they read twenty diffs in one sitting: missing tests, inconsistent naming, security smells, and TODO comments that should have been tickets.

An AI coding assistant does not get tired. It does not skip the obvious. That makes it ideal for a first-pass scan that runs before a human opens the diff. The goal is not to replace human judgment; it is to surface mechanical issues so reviewers spend time on the things that actually need experience.

In practice, first-pass AI review also creates a shared artifact. Instead of every reviewer catching different subsets of the same problems, the assistant produces one consistent list. That consistency matters more than precision; it is easier to ignore noise than to rediscover issues from scratch every review.

Prompt patterns for diff review

The prompt controls what the assistant catches. Treat it like a checklist that lives in your repo rather than an ad hoc request.

Basic review prompt

You are reviewing a pull request. Read the diff and check for: 1. Missing or weak test coverage on changed behavior. 2. Error handling gaps: swallowed exceptions, unchecked nulls, unbounded retries. 3. Security risks: hardcoded secrets, unsafe deserialization, SQL string concatenation. 4. Consistency: naming, error messages, and log format drift. 5. TODOs or commented-out code that should be tracked as issues. Return issues as a list with file path, line context, severity, and suggested fix.

Focused review prompt

Review only the security surface of this diff. Look for: - Hardcoded API keys, tokens, or passwords. - Unsafe input handling in API routes, DB queries, and template rendering. - Auth or permission bypass risks in new endpoints. Do not flag style or naming issues.

Test-gap review prompt

Compare the changed code against existing tests. Identify: 1. New code paths with no test coverage. 2. Edge cases the current tests miss after this change. 3. Mocks that are now stale because the underlying interface changed. For each gap, suggest a test case name and one-line assertion goal.

Store these prompts in a .github/prompts/pr-review.md file so every contributor uses the same checklist. Version control gives you a changelog for what the team values over time.

Where to run the review

There are three practical places to inject AI review into a PR pipeline, and they differ in automation cost and output quality.

CI comment step: Run the assistant against the diff in GitHub Actions and post results as a PR comment. Lowest friction; output is visible to the whole team.
IDE inline review: Use Copilot or Cursor to scan the current diff before pushing. Fastest for solo developers, but the review is local and not shared.
Pre-merge bot: A small service pulls open PRs, builds a diff, and runs a review prompt. More setup, but consistent across all contributors.

Start with the CI comment step. It costs almost nothing and gives the team a shared artifact to iterate on.

Building a CI review step

The simplest implementation is a GitHub Action that posts a review comment. It needs three things: the diff, a prompt template, and an API call to an assistant.

Step	What it does
Checkout	Fetch the repo at PR head so the diff is current.
Diff extraction	Use `git diff origin/main...HEAD` or the GitHub `github/pull-request` event payload.
Review prompt	Inject the diff into one of the repo-stored prompt templates.
Assistant call	Send the prompt to Copilot, Cursor, or any assistant API with access to the PR context.
Post comment	Use the GitHub API to post the assistant response as a single PR comment.

Keep the comment unlabeled unless the assistant returns issues. A blank review comment on a clean diff is acceptable; it means the assistant found nothing mechanical to flag.

Reading the review output

AI review output is most useful when it is short, structured, and scannable. Ask the assistant to group issues by severity and file so reviewers can triage by area.

A good review output has four parts: a one-line verdict, a list of issues with context, a coverage note, and a test-gap section. If any part is missing, tighten the prompt rather than ignoring the gap.

Reviewers should treat AI review as a filter, not a verdict. If the assistant flags three things and one is real, that is still a win over manual review catching zero.

Failure modes

AI review fails in predictable ways. The assistant will hallucinate issues that do not exist, miss context-specific invariants, and generate suggestions that are syntactically correct but architecturally wrong.

The practical fix is to scope prompts tightly, verify output with grep or targeted tests, and treat AI review as a filter rather than a verdict. If the assistant flags three things and one is real, that is still a win over manual review catching zero.

Another failure mode is review fatigue: if the bot comments on every nit, humans start ignoring it. Restrict AI review to high-severity categories and let humans own style and naming.

Limits and notes

AI review is strongest on mechanical checks and weakest on intent. It will catch a missing null check; it will not tell you that a function is doing too many things. Use it as a first pass, not a final gate.

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