AI prompts for code reviews help US engineering teams turn a diff into a structured, defensible review in minutes instead of hours. Every template below is written around the way US teams actually review — PRs on GitHub, CODEOWNERS-triggered reviewers, required approvals before merge, and the human bandwidth crunch where a senior engineer might review 6–10 PRs a day. You paste in the diff (or a `git diff main` fragment), and the AI returns a review scoped to one dimension — security, performance, readability, or coverage — instead of a shapeless list of nitpicks.
The templates assume a US engineering context: PRs squashed on merge, semver-aware releases, dependency scanning via Dependabot or Renovate, OWASP Top 10 as the default security threat model, and style guides enforced by ruff/eslint/gofmt. That means the prompts push for review comments that reference specific line numbers, cite a specific rule or vulnerability class, and include a suggested fix — not opinions like 'this feels off.' The output is designed to paste directly into GitHub review comments.
This content is educational and not a substitute for a licensed security audit or a human reviewer with domain context. AI review of a diff sees the code but not the deployment environment, the customer contract, or the on-call implications. Treat these prompts as a fast first pass that surfaces obvious issues so the human reviewer can spend their time on architecture, product fit, and the subtle bugs an LLM will miss.
AI prompts for code reviews help US engineering teams turn a diff into a structured, defensible review in minutes instead of hours. Every template below is written around the way US teams actually review — PRs on GitHub, CODEOWNERS-triggered reviewers, required approvals before merge, and the human bandwidth crunch where a senior engineer might review 6–10 PRs a day. You paste in the diff (or a `git diff main` fragment), and the AI returns a review scoped to one dimension — security, performance, readability, or coverage — instead of a shapeless list of nitpicks.
The templates assume a US engineering context: PRs squashed on merge, semver-aware releases, dependency scanning via Dependabot or Renovate, OWASP Top 10 as the default security threat model, and style guides enforced by ruff/eslint/gofmt. That means the prompts push for review comments that reference specific line numbers, cite a specific rule or vulnerability class, and include a suggested fix — not opinions like 'this feels off.' The output is designed to paste directly into GitHub review comments.
This content is educational and not a substitute for a licensed security audit or a human reviewer with domain context. AI review of a diff sees the code but not the deployment environment, the customer contract, or the on-call implications. Treat these prompts as a fast first pass that surfaces obvious issues so the human reviewer can spend their time on architecture, product fit, and the subtle bugs an LLM will miss.
Guides, tips, and deep dives for this prompt category
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Read moreCopy any prompt below, paste into ChatGPT, Claude, Gemini, or Copilot, and fill in the placeholders in [brackets].
Act as a US senior software engineer reviewing code. Perform a security review of this diff against the OWASP Top 10 items [list items, e.g., A01 broken access control, A03 injection, A07 auth failures]: [paste diff]. For each item either flag a specific line and severity (critical/high/medium/low), or explicitly state 'not applicable' with a one-line reason. Suggest a concrete fix with a code snippet for anything flagged high or critical.
Act as a US senior software engineer reviewing code. Perform a performance review of this function on the hot path [describe QPS or dataset size]: [paste function]. Identify time complexity, allocations inside loops, blocking I/O that should be async, missing batching, and any DB call inside a for-loop. Rank findings by expected p99 latency impact and suggest one benchmark to run before merging.
Act as a US senior software engineer reviewing code. Perform a readability review of this module: [paste module]. Score each new or changed function on naming, single-responsibility, length (flag >40 lines), cyclomatic complexity (flag >10), and comment quality. Return one comment per function that includes the file:line, the specific issue, and a suggested rename or refactor.
Act as a US senior software engineer reviewing code. Perform a test coverage assessment of this PR: [paste diff plus the new/changed test file]. Build a table with columns: new function, has direct test, tests behavior or implementation, mock hygiene (correct patch target, not over-mocked). Flag any public function with no test and any test where the function under test is itself patched away.
Act as a US senior software engineer reviewing code. Write a diff summary for the reviewer with a risk level (low/medium/high) and a suggested time-to-review: [paste diff]. Cover: what changed (2 sentences), why (from PR body), the highest-risk file, external-facing behavior changes, and any test or migration steps needed. Output as a short comment I can post at the top of the PR.
Act as a US senior software engineer reviewing code. Write a PR description from these commit messages and this diff: [paste git log --oneline plus diff summary]. Use the sections: Context, Change, Testing, Risk, Rollback. Keep it under 300 words, name any breaking changes explicitly, and include a checklist of manual verification steps for the reviewer.
Act as a US senior software engineer reviewing code. Assess whether this PR introduces breaking changes: [paste diff and current version from CHANGELOG or package.json]. Check function signatures, exported types, HTTP endpoint shapes, database schema, and config keys. Return a semver recommendation (patch/minor/major) with a one-sentence justification per detected breakage.
Act as a US senior software engineer reviewing code. Perform a dependency review of this package.json (or requirements.txt) diff for CVEs and supply-chain risk: [paste diff plus `npm audit` or `pip-audit` output]. For each added or bumped package, list known CVEs with severity, last-commit recency, license, and a merge/hold recommendation. Flag any package added by a maintainer joined in the last 90 days.
Act as a US senior software engineer reviewing code. Check this diff for style consistency against our team guide: [paste guide summary or link key rules] and [paste diff]. Ignore anything the linter already enforces. Focus on team-specific patterns: error handling shape, logging keys, dependency injection style, and file/folder placement conventions. Return one comment per deviation with a suggested fix.
Act as a US senior software engineer reviewing code. Review docstring and comment completeness for this module: [paste module]. For every public function or class, check for a docstring covering purpose, args, returns, and raises. Flag missing or misleading comments, redundant comments that restate the code, and TODOs older than 90 days. Suggest a docstring rewrite for anything below standard.
Act as a US senior software engineer reviewing code. Perform an error-handling review of this diff: [paste diff]. Flag bare except clauses, swallowed exceptions with no logging, error messages that leak sensitive data, missing retries on transient failures, and inconsistent error types across the module. For each finding, suggest a concrete pattern (typed exceptions, structured logging, tenacity retry) that matches the codebase.
Act as a US senior software engineer reviewing code. Perform a thread-safety review for this concurrent code: [paste code with goroutines, threads, asyncio tasks, or shared state]. Identify data races, missing locks or channels, non-atomic read-modify-write, deadlock potential, and unbounded goroutine or task spawning. Recommend a specific synchronization primitive per finding and a small test to reproduce the race.
Act as a US senior software engineer reviewing code. Draft a review comment reply that pushes back respectfully on this feedback I received: [paste reviewer's comment and my code]. Acknowledge the point, present my counter-argument with one piece of evidence (benchmark, spec, or prior discussion), invite them to disagree, and propose a concrete resolution — merge as-is, adjust, or take offline. Keep it under 120 words, no passive aggression.
Act as a US senior software engineer reviewing code. Write a decline-PR-politely comment for this PR that we cannot merge: [paste PR title, description, and the reason it must be declined — out of scope, wrong approach, blocked by refactor, etc.]. Thank the contributor for the effort, name the specific reason clearly, and propose a concrete next step — reopen after [X], split into 2 PRs, or discuss in [channel]. Under 150 words.
Understanding the building blocks lets you adapt any prompt to your own creative direction.
Tell the AI who the output is for and what real workplace situation it should support.
Act as a federal program analyst preparing a plain-language memo for agency leadership.Name the exact deliverable: email, memo, checklist, SOP, meeting recap, training note, or status update.
Format the answer as a one-page briefing with bullets, risks, and next actions.Specify whether the output should sound official, executive-ready, plain-language, or employee-friendly.
Use a professional, neutral, public-sector tone suitable for a US agency audience.For government, HR, finance, healthcare, legal, and compliance workflows, accuracy guardrails matter more than clever wording.
Use only the facts below, flag assumptions, and include a section for items that need verification.Ask the model to surface uncertainty so the user can verify sensitive or official information before using it.
Before finalizing, list compliance risks, missing details, and any claims that need human review.Tested on this prompt category as of mid-2026. Ratings reflect quality for AI Prompts for Code Reviews specifically.
| Model | Best for | Rating |
|---|---|---|
| ChatGPT (GPT-4o / GPT-5) | Everyday drafting and summaries | |
| Claude Sonnet 4.5 | Long documents and policy | |
| Gemini 2.5 Pro | Grounded in Google workspace | |
| Copilot (M365) | Office 365 integration | |
| Perplexity | Answers with citations |
Ratings reflect suitability for this category. Free tiers available on all listed models. Last tested May 2026 by PromptSpace editors.
No — use AI to generate a draft review, then edit it before submitting. Strip anything the linter already covers, remove low-signal nits, and keep only comments that need human judgment. Unedited AI reviews feel spammy, erode trust with the author, and often flag non-issues that waste cycles.
Aim for under 400 lines of diff or under 2 hours of reviewer time. Larger PRs get rubber-stamped, catch fewer bugs, and stall in review. If a change is bigger, split it — a refactor PR first, a behavior PR second — or gate the risky part behind a feature flag so it can merge dark.
Acknowledge the point, share your reasoning with one piece of evidence (benchmark, doc link, prior discussion), and ask a specific question. If you still disagree after one round, escalate to a synchronous 15-minute call — text-only debates on GitHub cost more time than a quick chat. Never merge over an unresolved comment from a required reviewer.
Route the prompt through an approved enterprise endpoint (Azure OpenAI with private networking, Bedrock, or a self-hosted model), never a personal API key. Confirm your org has a data-processing agreement covering code and telemetry, and never paste code from a repo classified as restricted or export-controlled into a general-purpose chatbot.
The author runs it first as a self-review before requesting human review. That surfaces the obvious issues before consuming a reviewer's attention and demonstrates effort. Reviewers can then run a second AI pass with a different dimension (security if the author ran readability) so the two passes cover complementary ground.
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Start with three inputs before opening a prompt: (1) the diff or `git diff main..HEAD` output, (2) the PR description and any linked issue, and (3) the review dimension you want covered — security, performance, readability, tests, or breaking change. Paste those into the prompt so the AI has real code to critique. A prompt anchored to a 40-line diff plus 'PR adds a new /api/v2/users endpoint that accepts JSON body with user_id' returns a specific review; a generic 'review my code' returns generic filler.
Then post the AI output as a draft review, not individual comments — GitHub batches draft comments so you can edit and remove noise before the author sees them. Cut any nitpick that is enforced by CI (formatting, imports, missing type hints if your linter catches it) and keep only comments that require human judgment. That habit stops AI reviews from feeling like a firehose and keeps trust with the author.
Security review of a diff should walk the OWASP Top 10 explicitly: injection (SQL, command, LDAP), broken auth, sensitive data exposure, XXE, broken access control, security misconfiguration, XSS, insecure deserialization, using components with known vulnerabilities, and insufficient logging. The security-review prompt below produces one section per relevant OWASP item and either flags a specific line or explicitly notes 'not applicable, no user input parsed here' — which is what a senior reviewer actually wants to see.
Performance review is only worth the time on hot paths — request handlers on high-QPS endpoints, batch jobs on large datasets, tight loops in ETL, and DB queries inside N+1 patterns. The performance-review prompt asks the AI to identify the algorithmic complexity, spot allocations inside hot loops, flag missing indexes on new query patterns, and note where async or batching would collapse round-trips. It skips micro-optimization on cold code paths that would never move a p99 latency graph.
Readability review is not about style — that is what ruff and prettier are for. It is about naming, function length, cyclomatic complexity, misleading abstractions, and comments that lie. The readability prompt below evaluates each new function for a single responsibility, flags functions over 40 lines, and calls out names that require reading the body to understand (`process`, `handle`, `do_thing`).
Test coverage assessment on a PR should answer three questions: does the diff add tests, do those tests actually exercise the new lines, and are the tests testing the behavior (or just the implementation)? The coverage-assessment prompt below cross-references the diff against the added test file and produces a table of new functions vs test coverage, flagging any public function without a corresponding test. It also spots over-mocked tests where the function under test is patched away.
A review comment can save a PR or start a slow war. Tone matters — the reply prompts below produce comments that push back on feedback respectfully (acknowledge, disagree, offer evidence, invite discussion) or decline a PR politely (thank the contributor, name the specific reason, propose a concrete next step). US engineering culture rewards direct-but-warm; the templates avoid both passive aggression and blunt dismissal.
Dependency review of a package.json or requirements.txt diff should scan for CVEs, license changes, and unmaintained packages before looking at anything else. The dependency-review prompt uses the CVE identifiers you paste in (from `npm audit`, `pip-audit`, or Snyk output) plus a lookup on last-commit dates for the newly added packages, and returns a merge/hold decision with one-line reasoning per package. That keeps supply-chain risk visible on every PR instead of only during quarterly audits.
No — use AI to generate a draft review, then edit it before submitting. Strip anything the linter already covers, remove low-signal nits, and keep only comments that need human judgment. Unedited AI reviews feel spammy, erode trust with the author, and often flag non-issues that waste cycles.
Aim for under 400 lines of diff or under 2 hours of reviewer time. Larger PRs get rubber-stamped, catch fewer bugs, and stall in review. If a change is bigger, split it — a refactor PR first, a behavior PR second — or gate the risky part behind a feature flag so it can merge dark.
Acknowledge the point, share your reasoning with one piece of evidence (benchmark, doc link, prior discussion), and ask a specific question. If you still disagree after one round, escalate to a synchronous 15-minute call — text-only debates on GitHub cost more time than a quick chat. Never merge over an unresolved comment from a required reviewer.
Route the prompt through an approved enterprise endpoint (Azure OpenAI with private networking, Bedrock, or a self-hosted model), never a personal API key. Confirm your org has a data-processing agreement covering code and telemetry, and never paste code from a repo classified as restricted or export-controlled into a general-purpose chatbot.
The author runs it first as a self-review before requesting human review. That surfaces the obvious issues before consuming a reviewer's attention and demonstrates effort. Reviewers can then run a second AI pass with a different dimension (security if the author ran readability) so the two passes cover complementary ground.