Why this is hard to get right
Picture this: It's Thursday afternoon. You have four open pull requests queued for review. Each one is from a different engineer with a different background, and each touches a different part of the stack — a React hook refactor, a Node.js API endpoint, a database query optimization, and a new CI script.
You're a senior engineer. The reviews are your responsibility. But you're also two hours from a product demo you need to prepare for.
You open the first PR. The code is functional but the component re-renders on every keystroke, there's a missing aria-label on a button, and the variable names read like alphabet soup. You know exactly what's wrong. You don't know how to say it in a way that teaches without stinging.
So you type a quick "can you clean this up?" comment, merge what you can, and move on. The developer learns nothing. The accessibility bug ships. The pattern repeats in the next PR.
This is the daily reality for thousands of engineers. Code review is one of the highest-leverage activities in a software team — it's how knowledge transfers, how standards spread, and how junior developers grow into senior ones. But it's also one of the most time-intensive and emotionally loaded tasks in the engineering workflow.
Most engineers fall into one of two failure modes: they write terse, unhelpful comments that save time but teach nothing, or they write exhaustive essays that burn them out and overwhelm the recipient. Neither works.
AI can bridge the gap — but only if you tell it what you actually need. A vague prompt produces vague comments. The AI doesn't know your team's seniority mix, your codebase conventions, the PR's goal, or whether "this is wrong" should be said as a blocker or a gentle nudge.
That's the gap a structured prompt fills. With the right context in place, AI-generated review comments become a force multiplier: they're thorough, they're calibrated, and they're ready to paste — so you spend your 45 minutes on judgment, not prose.
Common mistakes to avoid
Pasting Code Without Explaining the PR Goal
When the AI doesn't know what the PR is trying to accomplish, it reviews code in a vacuum. It may flag stylistic preferences while missing a logic error that only makes sense in the context of the feature being built. Always state the PR's intent.
Omitting the Author's Experience Level
A comment appropriate for a junior engineer ('here's why this pattern is problematic') sounds condescending to a senior. Skipping seniority context forces the AI to guess, producing feedback that lands badly on real teams.
Not Specifying Your Team's Standards
Generic 'best practices' feedback often contradicts the conventions your team already agreed on. If your team uses
snake_casefor database fields or has a specific error-handling pattern, the AI needs to know — otherwise it argues against your own rules.Asking for Comments Without a Severity System
A flat list of comments forces reviewers to re-triage everything before posting. Without severity labels, blocking bugs and minor nits look equally urgent. Specify a labeling system (e.g., blocking / suggestion / nit) to get review-ready output.
Forgetting to Request Explanations, Not Just Fixes
Asking the AI to 'point out issues' produces a bug list. Asking it to explain why each issue matters turns the review into a learning moment. The difference is one sentence in the prompt — but it's the difference between a correction and coaching.
The transformation
Review this code and give me some comments to paste into GitHub.
**Act as a senior software engineer conducting an async pull request review** on a TypeScript React codebase for a mid-sized B2B SaaS team. **Context:** - Reviewer: senior engineer; author: mid-level engineer (2-3 years experience) - PR goal: add a reusable `<DataTable>` component with sorting and pagination - Team uses conventional commits and follows Airbnb ESLint rules **Generate inline review comments that:** 1. Identify logic bugs, performance issues, and accessibility gaps 2. Explain **why** each issue matters, not just what to change 3. Suggest a concrete fix or code snippet for each issue 4. Separate blocking issues from non-blocking suggestions clearly 5. Keep tone constructive and educational — no condescension **Format:** GitHub Markdown, grouped by file, with severity labels: `[blocking]`, `[suggestion]`, `[nit]`
Why this works
Calibration
Specifying the author's seniority level calibrates every comment's depth and tone. The AI knows to explain foundational concepts for junior engineers but skip the basics for seniors, producing feedback that feels human and considered.
Scope
Stating the PR's goal anchors the review. The AI can distinguish between code that's technically suboptimal but out of scope versus code that directly undermines the PR's stated purpose — a distinction most generic review prompts miss entirely.
Structure
Requesting GitHub Markdown with severity labels transforms the AI output into something paste-ready. Structure isn't cosmetic — it's the difference between output you use immediately and output you spend 20 minutes reformatting.
Purpose
Framing the review as 'educational' shifts the AI's output from correction to coaching. Comments become explanations, not criticisms. This framing produces feedback developers act on rather than argue about.
Grounding
Referencing specific standards (Airbnb ESLint, conventional commits) grounds feedback in rules the team already accepted. This removes the 'that's just your opinion' objection and makes the AI's suggestions harder to dismiss.
The framework behind the prompt
Effective code review is grounded in two bodies of research: cognitive load theory and feedback psychology.
Cognitive load theory, developed by John Sweller, tells us that reviewers and recipients both have limited working memory. Review comments that explain why a change is needed — not just what to change — reduce the recipient's cognitive load by providing the reasoning they'd otherwise have to reconstruct themselves. This is why the most effective review comments are brief explanations, not just corrections.
Feedback psychology research (drawing on Carol Dweck's growth mindset work) shows that feedback framed as a learning opportunity produces better behavioral change than feedback framed as criticism. For code review, this means comments that teach a principle outlast comments that fix a bug.
The Conventional Comments framework (by Paul Slater) formalizes this with a labeling system — praise, nitpick, suggestion, issue, question — that forces reviewers to distinguish between blocking concerns and stylistic preferences. This maps directly to the severity labeling approach in the optimized prompt.
Finally, async communication principles (established in remote work research by GitLab and Basecamp) emphasize that written feedback must be self-contained. Unlike in-person reviews, async comments can't rely on tone of voice, follow-up questions, or body language. Every comment must carry its own context — which is exactly what a well-structured AI prompt is designed to produce.
Prompt variations
Act as a security-focused senior backend engineer reviewing a Python FastAPI pull request from a mid-level developer.
PR context: New user authentication endpoint using JWT tokens and bcrypt password hashing.
Generate review comments that:
- Flag security vulnerabilities (injection risks, token expiry handling, secret exposure)
- Check for OWASP Top 10 violations relevant to authentication
- Verify input validation and error message safety (no stack traces to clients)
- Confirm rate limiting considerations are addressed
Tone: Firm on security issues, collaborative on implementation choices.
Format: GitHub Markdown, labeled [critical], [security], or [improvement]
Act as a staff engineer specializing in backend performance reviewing a Go pull request that introduces a new database query layer.
Context:
- Production system processes 50,000 requests/hour
- PostgreSQL with existing indexes on
user_idandcreated_at - Author is a senior engineer familiar with Go but new to this codebase
Review for:
- N+1 query patterns and missing index usage
- Connection pool exhaustion risks
- Goroutine leaks or blocking calls in hot paths
- Benchmark test coverage for critical paths
Format: GitHub Markdown with [perf-critical], [perf-suggestion], and [readability] labels.
Act as a welcoming senior engineer reviewing a new hire's first pull request to a React TypeScript codebase.
Context:
- Author joined 2 weeks ago; this is their first production PR
- PR adds a simple form validation utility function
- Team values psychological safety and growth mindset
Generate review comments that:
- Lead with 2-3 genuine positives before raising issues
- Frame every issue as a learning opportunity with a 'why this matters' explanation
- Offer a complete code snippet for any suggested change
- Avoid jargon — explain acronyms (DRY, SRP) when you use them
Tone: Encouraging, specific, and non-judgmental throughout.
When to use this prompt
Senior Engineers Reviewing Junior PRs
Senior engineers can generate complete, tone-calibrated review comments for complex PRs without spending 45 minutes writing explanations from scratch. The AI handles the prose; the engineer handles the judgment.
Engineering Managers Coaching Teams
Engineering managers use structured review comments to reinforce team standards consistently across reviewers, reducing the variability in feedback quality that slows down junior developer growth.
Remote-First and Async Teams
Distributed teams where reviewers and authors work in different time zones need written comments that are self-contained and unambiguous. AI-generated comments with embedded explanations reduce costly async back-and-forth.
Open Source Maintainers
OSS maintainers reviewing contributions from unknown authors need comments that are welcoming yet precise. A well-prompted AI produces community-friendly feedback at scale without burning out core contributors.
Developer Experience Teams
DevEx or platform teams establishing review standards can use this prompt pattern to generate example comments that illustrate what 'good' looks like — making review guidelines tangible and actionable.
Pro tips
- 1
Specify the author's seniority level explicitly — the AI adjusts explanation depth and tone dramatically based on whether it's reviewing a junior, mid-level, or senior engineer's code.
- 2
Include your team's specific standards (ESLint config, style guide, naming conventions) so feedback references rules the author already agreed to follow, not abstract best practices.
- 3
List the PR's stated goal or ticket number context so the AI can flag scope creep — comments on code that technically works but doesn't belong in this PR are often the most valuable.
- 4
Add a 'positive reinforcement' instruction (e.g., 'note 1-2 things the author did well') to make the output feel like a real senior engineer's review, not a bug list.
The single biggest lever for improving AI-generated review comments is the quality of code context you provide. Here's a repeatable structure that works:
1. File path and purpose
Start every code block with a comment like // src/components/DataTable.tsx — reusable table with sort/filter. This tells the AI where the file lives in the architecture and what it's supposed to do.
2. Dependencies and imports
Include the import block even if you're not reviewing it. The AI uses imported libraries to infer patterns — seeing import { useQuery } from '@tanstack/react-query' tells it to evaluate caching behavior, not just render logic.
3. Related test files If test files exist, paste them alongside the implementation. The AI can then flag when tests don't cover the new code paths introduced in the PR — one of the most commonly missed review gaps.
4. The diff, not just the file Where possible, provide the Git diff rather than the full file. This focuses the AI on what changed rather than auditing the entire pre-existing codebase, which keeps comments relevant to the PR's scope.
5. Known constraints Mention performance budgets, browser support requirements, or API contract constraints upfront. Without this, the AI may suggest technically correct changes that violate real-world requirements your team has already agreed on.
Choosing a consistent severity labeling system makes your AI-generated comments immediately usable in real PR workflows. Here are three proven systems and when to use each:
Simple three-tier (recommended for most teams):
[blocking]— Must fix before merge; correctness, security, or data integrity at risk[suggestion]— Should fix; improves quality but won't break anything if deferred[nit]— Minor style or preference; author's call
This system maps directly to how most GitHub/GitLab teams already think about review comments.
Security-augmented system (for security-sensitive codebases):
[critical]— Security vulnerability requiring immediate fix[blocking]— Functional bug that must be resolved[hardening]— Defensive improvement worth doing now[suggestion]/[nit]— as above
Learning-focused system (for teams with junior developers):
[required]— Must change before merge[learn]— Not blocking, but explaining a better pattern for their growth[praise]— Explicitly calling out something done well
Add your chosen system to the prompt's format section and the AI will apply it consistently across all generated comments.
The type of PR should change how you frame the review prompt. Treating a refactor the same as a greenfield feature produces misaligned feedback.
For refactors: Add this instruction: 'The primary review goal is behavioral equivalence. Flag any change that alters observable behavior, even if the new behavior is arguably better.' This focuses the AI on regression risk rather than design improvements — which is exactly what refactor reviews are for.
For new features: Add: 'Evaluate whether the implementation satisfies the stated feature requirements, not just whether the code is clean.' This prompts the AI to think like a QA engineer and a code reviewer simultaneously.
For bug fixes: Add: 'For each change, assess whether it fixes the root cause or only the symptom. Flag any change that might introduce a regression in adjacent functionality.'
For dependency upgrades: Add: 'Review for breaking API changes between the old and new version. Reference the library's changelog conventions where relevant.'
Matching the review framing to the PR type is the most underused technique in AI-assisted code review. A single sentence addition to your prompt produces dramatically more relevant output.
When not to use this prompt
This prompt pattern works best when you provide real code. If you're still in the design or pseudocode phase, a code review prompt will produce surface-level feedback that misses architectural concerns. Use an architecture review or design document prompt instead.
This approach also isn't a substitute for human judgment on security-critical code. Use AI-generated comments as a first pass or checklist supplement — not as the final word on authentication, cryptography, or data handling decisions. Always have a human security-minded reviewer sign off on those paths.
Troubleshooting
AI comments are too generic and don't reference the actual code
Paste the real code block directly into the prompt rather than describing it. Add file paths as inline comments at the top of each block. If the PR is large, submit one file at a time, each with the same context header, so the AI focuses on specific implementation details rather than abstract patterns.
Tone is too harsh or reads as condescending for junior authors
Add an explicit tone instruction: 'Frame every issue as a question or suggestion, never a command. Use first-person plural where possible (e.g., "we usually prefer" instead of "you should").' Also add a 'positive reinforcement' instruction asking for at least two genuine strengths before any critical comments.
AI flags issues that violate your team's established conventions
Add a 'Do Not Flag' section to your prompt listing conventions the AI should accept, not challenge. For example: 'Our team uses named default exports by convention — do not suggest converting to named exports.' This prevents the AI from arguing against decisions your team has already made and documented.
How to measure success
A successful AI-generated code review produces comments you can paste into GitHub with minimal editing. Look for these quality signals: each comment includes a specific line reference or code block, every issue comes with a 'why this matters' explanation of at least one sentence, blocking issues are clearly distinguished from stylistic suggestions, and the tone stays consistent throughout — neither sycophantic nor harsh. If you find yourself rewriting more than 20% of the generated comments, revisit your context inputs — the AI likely lacked seniority level, PR goal, or team standards to calibrate correctly.
Now try it on something of your own
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constructive async pull request review comments
Try one of these
Frequently asked questions
Yes — and you should. Add the code block directly after the context section of the prompt. The AI produces far more specific comments when it sees the real implementation rather than a description of it. For large PRs, break it into file-by-file submissions.
Add a 'Standards' bullet to the context section listing your specific rules — naming conventions, preferred patterns, banned libraries, or a link description to your internal guide. The more specific you are, the more the AI's feedback will sound like it came from a teammate who knows your codebase.
Yes, if you name them explicitly. Include the framework version and any relevant constraints (e.g., 'React 18 with concurrent features enabled' or 'Next.js App Router — no getServerSideProps'). This prevents the AI from suggesting patterns that work in one version but break in another.
Break the review into focused batches by concern: data layer first, then business logic, then UI components. Submit each batch as a separate prompt with the same context header. This produces more focused feedback than overwhelming the AI with an entire diff at once.
Add a 'known risk areas' line to your prompt — for example, 'pay particular attention to race conditions in the async data fetching logic.' Directing the AI's attention to where complexity lives significantly improves the quality of bug-finding in those areas.