TL;DR: Here are 35 AI prompts for code review, debugging, and refactoring, organized into three targeted passes: review (security, performance, readability), debugging (error analysis, root cause), and behavior-preserving refactoring. The key insight most prompt lists skip is that running separate passes on the same code catches more issues than one combined review. Every prompt uses [bracketed variables] you fill in with your stack details.
What are the best AI prompts for code review in 2026?
The best AI prompts for code review are targeted by dimension, not general requests for "all feedback." Asking an AI to "review this code" produces surface-level observations across every category at once. Asking it to "review this code specifically for authentication and authorization gaps, citing every finding with a line number and severity" produces depth that is actually useful in a PR. AI prompts for code review work best when you treat the model as a specialized reviewer you brief with a focused scope, not a generalist scanning everything simultaneously.
This guide gives you 35 prompts organized across three passes: review (security, performance, readability), debugging (error analysis, root cause tracing), and refactoring (behavior-preserving rewrites). Our guide on writing better ChatGPT prompts explains the underlying structure; 25 ChatGPT Prompts for Developers covers the broader developer workflow including code generation and architecture planning. This post is the reference you reach for when you are reviewing or debugging code that already exists. For saving and reusing these prompts with your stack details pre-filled, see how reusable prompt variables reduce the setup time per review to under a minute.
What prompt structure works for code review and debugging?
Every prompt in this guide follows what we call the five-component code prompt system: role, context, task, constraints, and output format. Remove any one component and the model fills the gap with assumptions that rarely match your actual stack or review goal.
| Component | What to include | Why it matters |
|---|---|---|
| Role | "Act as a senior [language] security engineer" | Anchors the model's review lens and vocabulary |
| Context | Language, framework, version, code purpose | Without this, review is abstract and generic |
| Task | Specific review type: security, performance, refactor | Scopes feedback to the dimension that matters |
| Constraints | "Flag with line numbers; rank by severity" | Makes output immediately actionable |
| Output format | Numbered list, table, or structured report | Controls how feedback is delivered |
The same five components apply whether you are asking for a security review, a root cause analysis, or a refactoring plan. The difference is only what you put in the task and constraints fields. If you use these prompts repeatedly, saving them to a prompt library with your stack variables pre-filled cuts the setup time from two minutes to ten seconds per review.
How do I use AI for security-focused code review?
A security pass finds a different class of bugs than a performance pass. Run them separately on the same code. These eight prompts cover the most common security review scopes, from general audits through specific vulnerability categories.
1. General security audit
Act as a senior [language] security engineer.
Code: [paste code]
Framework: [framework], version [X].
Perform a security audit across these categories:
input validation, authentication and authorization, data exposure, injection risks, secrets handling.
For each issue: line number, vulnerability type, severity (critical/high/medium/low), and a one-sentence fix.
Output: sorted by severity, highest first.
Run this as your first security pass on any new feature branch. The severity sort tells you what to fix before the next deploy versus what can wait for the next sprint.
2. Input validation check
Act as a security reviewer.
Code: [paste code — focus on functions that accept user input]
Language: [language], framework: [framework].
Review every input path: form fields, API parameters, URL parameters, file uploads.
For each: is the input validated before use? Is it sanitized before storage or output?
What is the injection risk if it is not?
Output: table — input name | validation status | risk | recommended fix.
The table format is deliberate here. It produces a checklist you can assign directly in your issue tracker rather than a paragraph the team has to parse.
3. Authentication and authorization gap analysis
Act as a senior security engineer specializing in auth systems.
Code: [paste auth-related code]
Auth method: [JWT / OAuth / session cookie / API key].
Identify: missing token validation, privilege escalation paths, broken access controls, and session fixation risks.
For each gap: describe the attack vector and the minimum fix required.
Do not suggest a rewrite — identify the minimum change that closes each gap.
The "minimum change" constraint is important: without it, the model tends to propose replacing your entire auth layer rather than addressing the specific gaps.
4. SQL and NoSQL injection risk scan
Act as a database security reviewer.
Code: [paste database query code]
Database: [PostgreSQL / MySQL / MongoDB / other].
ORM or raw queries: [ORM name / raw SQL].
Identify every query that constructs SQL or document filters from user input without parameterization.
Output: line number, query pattern, risk level, and the parameterized replacement.
5. API key and secrets exposure check
Review this code for secrets exposure.
Code: [paste code]
Check for: hardcoded API keys, tokens, passwords, or credentials in strings, comments, or config objects.
Also check for: environment variable references that might log secrets, and debug output that exposes sensitive values.
Output: line number, what is exposed, and recommended remediation.
This prompt is worth running on every file that touches configuration, environment setup, or third-party integrations.
6. Dependency vulnerability flag
Act as a security reviewer.
Dependency list: [paste package.json / requirements.txt / go.mod / Gemfile]
Flag any package names that are commonly associated with known vulnerabilities or abandoned maintenance.
Note: I will verify current CVE status independently — flag candidates for review, do not fabricate CVE numbers.
Output: package name, concern, and verification note.
7. OWASP Top 10 coverage check
Act as an OWASP-focused security reviewer.
Code: [paste code]
Stack: [language, framework, deployment target].
Map this code against the OWASP Top 10. For each category, state: covered, partially covered, or not covered.
For partially covered and not covered: describe the specific gap and what would close it.
8. Rate limiting and denial-of-service surface
Act as a security architect.
Code: [paste API endpoint or route handler code]
Framework: [framework].
Identify: missing rate limits, unauthenticated endpoints with expensive operations,
and patterns that allow resource exhaustion.
For each finding: describe the attack scenario and the minimum viable mitigation.
How do I catch performance problems with AI prompts?
Performance review prompts produce better results when you ask the model to rank bottlenecks by impact and explain the algorithmic complexity tradeoff. "What's slow?" produces a list; "rank these bottlenecks by likely impact on [your expected load]" produces a prioritized action plan.
9. General performance bottleneck scan
Act as a performance engineer.
Code: [paste code]
Language: [language], runtime: [Node / Python / JVM / Go / other].
Expected load: [requests per second or dataset size context].
Identify performance bottlenecks, ranked by likely impact on production performance.
For each: describe the problem, its complexity in Big-O notation, and a more efficient alternative with trade-offs.
10. Database query performance review
Act as a database performance reviewer.
Code: [paste ORM calls or raw queries]
Database: [PostgreSQL / MySQL / MongoDB].
Table sizes: [approximate — e.g., "users table: ~1M rows"].
Identify: N+1 query patterns, missing index hints, unnecessary joins, and queries that scan full tables.
For each: suggest a specific fix and estimate the approximate performance gain.
Providing table size context changes the model's recommendations significantly. A missing index on a 10,000-row table is a low-priority finding; the same missing index on a 10-million-row table is critical.
11. Memory usage and leak detection
Act as a memory profiling reviewer.
Code: [paste code]
Language: [language] (runtime: [e.g., V8, CPython, JVM]).
Identify: objects retained beyond their useful lifetime, event listener accumulation,
circular references that prevent garbage collection, and large allocations in hot paths.
For each: describe the pattern and a specific fix.
Memory issues are among the hardest to reproduce in development because they accumulate slowly and only become visible under sustained production load. Running this prompt before a feature ships surfaces patterns the model can recognize structurally — event listeners added inside loops, closures capturing large objects — that would not show up in unit tests.
12. Algorithm complexity review
Review the algorithmic complexity of this code.
Code: [paste code]
For each function or loop: state the time and space complexity in Big-O notation.
Flag any O(n²) or worse pattern and suggest an alternative with better complexity.
Show the before and after complexity for each suggestion.
The "show before and after complexity" instruction is what converts this from an analysis into an actionable change. Without it, the model often describes the problem in general terms without committing to a specific alternative, leaving you to infer what "use a hash map instead" actually means in context.
13. Async and concurrency review
Act as a concurrency engineer.
Code: [paste async/concurrent code]
Language: [language], async model: [async/await / Promise / goroutine / thread pool].
Identify: race conditions, missing awaits on async calls, sequential operations that could run in parallel,
and unhandled rejection or goroutine leak patterns.
For each: describe the risk and the fix.
Specify the async model explicitly. What counts as a race condition in a goroutine-based system differs from what counts as one in an async/await Promise chain. Without this context, the model applies generic concurrency advice that may not map to your runtime.
14. Caching opportunity analysis
Act as a performance architect.
Code: [paste data-fetching or computation code]
Stack: [language, framework, cache layer if any: Redis / in-memory / none].
Identify operations that are good candidates for caching.
For each: describe the data, why it is cacheable, the suggested cache strategy (TTL, invalidation),
and the estimated reduction in compute or database load.
Include whether you currently have a cache layer in the stack field. If you do, the model suggests cache keys and TTL values calibrated to your existing infrastructure. If you do not, it suggests which layer to add first and what it would unblock.
How do I use AI to review for readability and maintainability?
Readability prompts catch the problems that accumulate into technical debt. They are most useful before a code review cycle, giving the author a chance to clean up before peers read the code. The key is specifying your team's conventions in the prompt so the model reviews against your actual standards, not general best practices.
15. General readability review
Act as a senior developer doing a readability review.
Code: [paste code]
Language: [language], style conventions: [describe key rules, or "standard [language] conventions"].
Review for: unclear naming, oversized functions, missing or misleading comments,
and logic that takes more than 30 seconds to follow.
For each issue: cite the line, describe what is unclear, and suggest a specific improvement.
Including your team's style conventions in the prompt is what separates a useful readability review from generic feedback. Without them, the model reviews against the average of the internet's style preferences, which may contradict your team's deliberate choices.
16. Function complexity and single-responsibility check
Review this code for function complexity.
Code: [paste code]
Flag any function that: exceeds 30 lines, has more than 3 levels of nesting, does more than one distinct job,
or has more than 4 parameters.
For each flagged function: describe what it is actually doing, how to split it,
and what to name each proposed sub-function.
The instruction to name the proposed sub-functions is the part most developers skip when writing this prompt themselves. Without it, you get a recommendation to "extract this logic into a separate function" with no suggestion of what to call it — which puts the naming work back on you.
17. Naming review
Act as a senior developer.
Code: [paste code]
Language: [language].
Review variable, function, and class names for clarity.
Flag any name where the purpose is not clear from the name alone.
For each flagged name: suggest a more descriptive alternative and explain why it is clearer.
Do not flag names that are clear and conventional (e.g., i in a simple loop).
The "do not flag conventional names" instruction prevents the model from suggesting that you rename loop variables and other standard idioms. Without this constraint, naming reviews often produce noise that drowns out the genuinely ambiguous names worth fixing.
18. Documentation and comment quality review
Review the documentation and comments in this code.
Code: [paste code]
Flag: missing docstrings on public functions, comments that describe what the code does
instead of why, and outdated comments that no longer match the implementation.
For each: provide a corrected or new version.
Asking the model to provide the corrected version, not just flag the problem, makes this prompt immediately actionable. You can accept, modify, or reject each suggestion rather than rewriting documentation from scratch.
19. DRY principles and duplication check
Act as a code quality reviewer.
Code: [paste code — include multiple files if reviewing cross-file duplication]
Identify: repeated logic blocks, duplicated data transformations, and copy-pasted error handling.
For each duplication: describe the pattern, where it appears, and how to extract it into a shared function.
Paste multiple files when you suspect cross-file duplication. The model can identify the same transformation appearing in different modules only if it can see all the modules simultaneously. Single-file review misses the most common form of duplication in larger codebases.
20. Error handling completeness review
Act as a resilience reviewer.
Code: [paste code]
Language: [language], framework: [framework].
Identify: uncaught exceptions, swallowed errors (empty catch blocks), missing null checks,
and error messages that expose internal stack traces to end users.
For each: describe the gap, the failure scenario it creates, and a specific fix.
The "describe the failure scenario" instruction is what makes this review useful to someone who has never thought about a specific error path. A bare list of uncaught exceptions is easy to dismiss; a description of "this function will crash the entire request handler if the database is unavailable, returning a 500 with the full stack trace" is not.
What AI prompts help debug errors and trace root causes?
Debugging prompts produce better results when you include the full error message, the complete stack trace, and the framework versions. The goal is root cause explanation first, then the minimum fix. Models that explain root causes produce fewer regression patches than models that patch symptoms.
21. Error message analysis
Act as a senior [language] developer.
Error: [paste full error message]
Stack trace: [paste full stack trace]
Code: [paste the relevant function or file]
Language: [language], framework: [framework], version: [X].
Step 1: Explain the root cause in plain English.
Step 2: Suggest the minimum fix.
Step 3: List any related issues that could surface after this fix.
The three-step structure is deliberate. It stops the model from jumping straight to a patch and forces it to articulate the actual cause first.
22. Stack trace interpretation
Explain this stack trace step by step.
Stack trace: [paste]
Language: [language], framework: [framework].
Walk through the call chain from the entry point to the failure.
Identify where control left expected behavior and what condition triggered the error.
This prompt is especially useful when the error originates in a library you did not write. Walking through the call chain from your entry point to the failure in a third-party module explains which of your assumptions triggered the library's error condition.
23. Root cause with 5-Whys analysis
Act as a debugging engineer.
Symptom: [describe what is going wrong — what the user sees or what the logs show]
Code: [paste relevant code]
Apply the 5-Whys method: ask "why" five times, each time going one level deeper into the cause.
State the root cause at the end and distinguish it from the proximate cause.
This prompt works especially well for bugs that have been patched multiple times without the team finding the real cause. The 5-Whys framing explicitly separates symptom from root cause.
24. Race condition and async bug analysis
Act as a concurrency expert.
Code: [paste async/concurrent code]
Observed behavior: [describe the intermittent or non-deterministic bug]
Language: [language], async model: [async/await / goroutine / thread pool].
Identify where the bug could arise from timing or ordering issues.
Describe the exact sequence of operations that triggers the failure.
Suggest a fix and explain why it prevents the race.
The "describe the exact sequence" instruction distinguishes a useful response from a generic warning about concurrency. Knowing "the race occurs when goroutine A reads the map key between goroutine B's delete and goroutine C's write, producing a nil pointer dereference" is actionable. "This code may have race conditions" is not.
25. Regression analysis
Act as a debugging engineer.
Behavior before change: [describe what worked]
Behavior after change: [describe what broke]
Code changes: [paste diff or describe what changed]
Identify which change introduced the regression and explain the mechanism.
Suggest a minimal fix that restores the original behavior without reverting the entire change.
The "minimal fix without reverting" instruction is important when the change that introduced the regression was itself correct and needed. Reverting is often faster in the short term but throws away the intentional improvement. A minimal fix preserves the intent of the change while correcting the side effect.
26. API contract mismatch debugging
Act as an API debugging specialist.
Endpoint: [URL and HTTP method]
Expected request/response: [paste schema or example]
Actual request/response: [paste what is being sent and received]
Stack: [client language / server language, frameworks].
Identify the mismatch. Explain which side is out of contract and what change corrects it.
Pasting both the expected and actual payloads is the instruction most developers skip, leaving the model to guess which schema is authoritative. With both provided, the model can tell you precisely which field is missing, mistyped, or in the wrong format, and which side of the contract needs to change.
27. Silent failure detection
Act as a debugging engineer.
Code: [paste code — especially functions that might fail silently]
Language: [language].
Identify: return values that are ignored, errors caught and discarded,
async calls not awaited, and conditions where the function returns without signaling failure.
For each: describe the silent failure mode and how to make it observable.
Silent failures are the hardest bugs to diagnose because they produce no error output. This prompt specifically looks for the patterns that hide them.
28. Variable state tracing
Act as a debugging engineer.
Code: [paste code]
Function: [function name]
Trace the state of every variable at each step through this function.
Input for this trace: [describe input or paste test case].
Identify at which step the actual state diverges from the expected state.
How do I refactor code with AI without changing existing behavior?
The behavior-preservation constraint is the most important element of any refactoring prompt. Without it, the model treats "improve" as permission to change behavior along with structure. Every refactoring prompt below includes an explicit constraint. Add a test checklist request to any prompt where you want confidence before merging.
29. Behavior-preserving refactor
Act as a senior [language] developer.
Code: [paste code]
Task: Refactor this code for [clarity / reduced complexity / better naming / modularity].
CONSTRAINT: Do not change any observable behavior or output.
For each change: state what you changed, confirm what behavior is preserved,
and flag any change where you are uncertain about preservation.
Also provide a test checklist I can run to verify the refactor is safe.
30. Extract function refactor
Act as a refactoring engineer.
Code: [paste code]
Identify logical units within this code that should be extracted into separate named functions.
For each extraction: name the function, define its inputs and outputs,
write the extracted function, and show the calling code after extraction.
Constraint: no change to observable behavior.
Asking the model to show the calling code after extraction is what makes this prompt produce a complete refactor rather than just the extracted function. Without seeing the updated call site, you have to infer how to wire the extraction back in.
31. Design pattern suggestion
Act as a software architect.
Code: [paste code]
Language: [language].
Identify design patterns (Gang of Four or common [language] idioms) that would simplify this code
without over-engineering it.
For each pattern: explain what it solves here, show the refactored version,
and note any trade-offs in complexity or testability.
The "without over-engineering it" constraint is the most important field in this prompt. Without it, models frequently recommend patterns that are theoretically correct but add abstraction layers that make a simple function harder to follow, not easier. The constraint forces the model to justify each pattern in terms of the actual complexity it reduces.
32. Dependency injection refactor
Act as a senior [language] developer.
Code: [paste code with tightly coupled dependencies]
Framework: [framework].
Refactor to inject dependencies via constructor or function parameter instead of creating them internally.
For each change: explain what was tightly coupled, show the injected version,
and note what this enables for testing.
The "note what this enables for testing" instruction connects the refactoring to a concrete benefit. A developer who was not convinced a dependency injection refactor was worth doing will reconsider when they see the specific test scenarios it unlocks.
33. Error handling upgrade
Act as a resilience engineer.
Code: [paste code with existing error handling]
Language: [language], framework: [framework].
Upgrade error handling to: distinguish recoverable from fatal errors,
add structured error logging with context, and use typed errors instead of generic strings.
Constraint: preserve all existing error messages exposed to callers — only add to them, do not remove.
The preservation constraint is critical when your error messages are part of a client contract. Changing "invalid_token" to a typed error class is a safe internal refactor; changing the string exposed to API consumers is a breaking change. The constraint explicitly prevents the model from conflating the two.
34. Type safety improvement
Act as a [TypeScript / Go / Rust / other typed language] developer.
Code: [paste code]
Add or improve type annotations to eliminate implicit any, untyped parameters, and missing return types.
For each change: explain what type information you added and what class of bug it prevents.
Constraint: no behavior changes — type annotations only.
The "explain what class of bug it prevents" instruction turns a mechanical annotation exercise into a code review artifact. The explanation is what makes a reviewer say "yes, this change is worth merging" rather than "I am not sure why we added 40 type annotations."
35. Dead code elimination
Act as a code cleanup engineer.
Code: [paste code]
Language: [language].
Identify: unused variables, unreachable code blocks, imports never used,
and commented-out code present for more than one commit.
For each: state whether it is safe to delete (explain why) or whether it might be needed (explain the uncertainty).
When should I run separate review passes versus one combined prompt?
Separate targeted passes catch more issues per category than one combined prompt. The trade-off is time. A combined prompt is faster to run but produces shallower feedback across every dimension. Separate passes take longer but produce output that is easier to assign, track, and act on.
Run separate passes when:
- You are reviewing code before a security-sensitive deployment
- You are debugging a problem that has resisted one or more previous fixes
- You are preparing a codebase for a new contributor or open-source release
Run a combined prompt when:
- You need a quick sanity check on a small change before requesting a peer review
- You are doing an early-stage proof-of-concept review where good enough beats thorough
- Time is the binding constraint
For security reviews on any code that touches user data, authentication, or payment flows, separate passes are not optional. A combined prompt that touches security, performance, and readability simultaneously typically surfaces one or two findings per category. Three targeted passes surface significantly more coverage per category, and the output is organized by type, which makes it far easier to prioritize and assign.
How Prompt Architects fits this workflow
All 35 prompts above work inside ChatGPT, Claude, or Gemini without any additional setup. What Prompt Architects adds is the infrastructure that makes them reusable across reviews: save any prompt to your library with your language, framework, and output format pre-filled, and the next code review starts in ten seconds rather than requiring a fresh setup each time.
For teams running structured code reviews, the JSON prompt output format is particularly useful. Generate a machine-readable review report from a security or performance pass, then feed that output directly into your issue tracker or CI pipeline without reformatting. The JSON prompts guide explains the format in detail.
To carry your prompt library into your IDE, the MCP integration connects your saved prompts to Cursor and Claude Desktop so you can trigger a code review prompt without leaving your editor. Teams using MCP alongside a shared library are our most engaged users by a significant margin (our customer data, July 2026).
"The prompt library is genius — I save structured prompts by category and reuse them. Clean UI, no bloat. Just does the thing." — info.webefo, Verified AppSumo review
Prompt Architects is free to start, no credit card required. Add the Chrome extension and your saved review prompts are one click away inside whichever AI tool you have open.
Pick the five prompts that match your most common review bottleneck, save them with your stack details filled in, and run them on your next pull request. The setup time is under five minutes; the payback is in every review after that.
Generate structured JSON code review reports with Prompt Architects — free to start →