Best AI Prompts for Code Review Automation with Cursor

TL;DR

• Cursor’s AI review capabilities work best when given specific focus areas rather than asked to review code generally.
• The most practical Cursor review workflows use AI for routine issues first, then escalate complex findings to human review.
• Cursor can generate unit tests alongside review findings, helping teams maintain coverage while improving quality.
• Review prompts that include the codebase context and coding standards produce significantly better output than generic review requests.
• The combination of AI review plus human oversight produces better results than either alone at a fraction of the human time cost.

Introduction

Code review is the backbone of software quality in team development. It is also one of the most consistently under-resourced activities — the review queue grows faster than senior developers can work through it, PRs sit waiting for review while features stall, and the pressure to move quickly leads to shallow reviews that miss real issues.

Cursor brings AI-assisted review directly into the IDE, making it practical to review every PR comprehensively without requiring senior developers to spend disproportionate time on routine review work. The key is knowing how to structure review requests so Cursor focuses on the highest-value issues and presents findings in a way that human reviewers can act on efficiently.

Table of Contents

1. Cursor’s Strengths in Code Review
2. Review Prompt Architecture
3. Focused Review Prompts
4. Bug Detection Prompts
5. Test Generation During Review
6. PR Description and Context Prompts
7. Review Workflow Patterns
8. Managing Review Quality
9. FAQ
10. Conclusion

1. Cursor’s Strengths in Code Review

Cursor’s review capabilities are integrated directly into the development environment, which changes the review workflow in practical ways.

IDE Context Awareness: Because Cursor operates within the IDE, it can access the full codebase context — function callers, import relationships, type definitions — during review. This means it can identify issues that require understanding how changed code is used, not just what it contains.

Real-Time Review: Cursor can review code as it is written, providing feedback before a PR is ever submitted. This shifts review from a gatekeeping function (checking code before it merges) to a coaching function (improving code as it is developed).

Bilateral Refactoring: Unlike external review tools, Cursor can both identify issues and implement fixes in the same context. A review comment that says “this should be refactored” becomes an immediate opportunity to show the refactored code.

Test Generation Integration: Cursor can generate tests for issues it identifies during review, closing the loop between issue detection and regression prevention in a single session.

2. Review Prompt Architecture

The quality of Cursor’s review output depends on how specifically you frame the review request.

The Structured Review Prompt: Cursor review prompts should specify: the code to review and its context, the specific concerns that warrant attention, the codebase conventions and standards that apply, and what output format serves the reviewer best. A complete structured prompt includes all four elements.

Code Context Prompt: “Here is a function I modified: [paste function]. Here is how it is used in the codebase: [describe callers, imports, and dependencies]. Here is my change: [describe change and why]. With this context in mind, review the change for: [specific concerns — correctness, performance, interface compatibility].”

Conventions-Aware Review Prompt: “Our codebase follows these conventions: [list conventions — naming patterns, error handling approach, async patterns, test structure]. Review the following changes against these conventions. Flag: any convention violations, any patterns where following convention would improve the code, and any cases where violating convention was intentional and appropriate.”

Output Format Prompt: “When you review this code, structure your output as: [summary — one sentence about what the PR does], [must-fix — issues that must be resolved before merge], [should-fix — issues that would significantly improve quality], [consider — issues that are optional improvements], [praise — what is done well]. For each issue: file, line, description, severity, and recommended fix.”

3. Focused Review Prompts

Rather than asking Cursor to review everything, the most effective approach is focused review on specific concerns.

Security-Focused Review Prompt: “Perform a security-focused review of the following changes. Specifically look for: SQL injection vectors (unsanitized inputs in queries), command injection (unsanitized inputs in system calls), authentication/authorization bypasses, sensitive data exposure (data returned to clients without filtering), hardcoded secrets, and insecure deserialization. For each finding: severity classification, attack scenario, and specific remediation.”

Performance-Focused Review Prompt: “Perform a performance review of the following changes. Look for: N+1 query patterns, unnecessary repeated computation, inefficient data structure usage, blocking I/O in async contexts, and unnecessary memory allocations. For each finding: estimated performance impact, the specific code location, and the optimization recommendation.”

Reliability-Focused Review Prompt: “Review this code for reliability issues. Look for: error handling that silently swallows exceptions, missing boundary condition checks, race conditions in concurrent code, resource leaks (files, connections, handles not properly managed), and lack of timeout handling on external calls. For each finding: the failure mode it prevents, the specific code location, and the recommended fix.”

API Contract Review Prompt: “Review the following changes for API contract violations. Check: are public API function signatures preserved (no breaking changes to parameters or return types)? Are side effects documented and consistent? Are error returns handled consistently by callers? Are backward compatibility requirements met? Flag any potential breaking changes with severity and migration recommendation.”

4. Bug Detection Prompts

Cursor’s pattern recognition capabilities catch common bug patterns efficiently.

Common Bug Pattern Review Prompt: “Review the following code for common bug patterns: null/undefined access, resource leaks, error swallowing, mutable default arguments, improper equality comparisons, and timezone handling bugs. For each potential bug: describe what you are seeing, explain the circumstances under which it would cause a problem, and provide a specific fix. Distinguish between theoretical issues (would only occur in unrealistic scenarios) and practical issues (likely to occur in production).”

Logic Error Detection Prompt: “Review the following code for logic errors. Look for: conditions that are always or never true, loops that never execute or execute infinitely, return statements that make subsequent code unreachable, variables used before initialization, and calculations with obvious off-by-one errors. For each potential logic error: describe the logic flow that leads to the problem, the expected behavior, and the correction.”

Async Bug Review Prompt: “Review the following async code for common async/await bugs: missing awaits, incorrect await ordering, async functions that fail to handle CancelledError, blocking calls inside async functions, and deadlocks from circular awaits. For each: describe the bug pattern, the scenario that triggers it, and the fix.”

5. Test Generation During Review

One of Cursor’s most practical capabilities is generating tests for issues identified during review.

Test Generation for Issues Prompt: “You identified the following issues in your review: [list issues]. For each issue, generate a unit test that would fail if the bug were present and pass when the fix is applied. The test should: be placed in [test file location], follow our test conventions [describe conventions], use descriptive names that explain what is being tested, and include comments explaining the expected behavior.”

Edge Case Test Generation Prompt: “Based on your review of the changed code, identify the top 5 edge cases that are not currently tested. For each edge case: write a test that covers it, explain why this edge case is important to test, and place the test in the appropriate test file following our conventions.”

Regression Test Prompt: “Generate regression tests for the following changes to prevent the identified issues from reoccurring. Focus on: the specific bug patterns found, the boundary conditions in the changed code, and the error paths that were found to be insufficiently handled. Each test should have: a descriptive name, clear setup and assertion, and a comment explaining what regression it prevents.”

6. PR Description and Context Prompts

Cursor can help reviewers quickly understand the purpose and scope of a PR.

Quick Context Prompt: “Before reviewing the code, summarize this PR: what does it do, why was this change needed, and what are the most important things a reviewer should focus on? Assume the reviewer has read the PR description but has not yet looked at the code.”

Dependency Impact Prompt: “This PR touches [file/module]. What other parts of the codebase might be affected by these changes? Are there dependent modules, shared utilities, or integration points that might be impacted? Where should reviewers from other teams be tagged for awareness?”

Risk Assessment Prompt: “Assess the risk of this PR from a deployment perspective: what is the blast radius if something goes wrong? Is this change safely backward compatible? Does it require any infrastructure changes, database migrations, or coordination with other teams? What is the rollback plan if issues are discovered after deployment?“

7. Review Workflow Patterns

Cursor review works best when integrated systematically into the team workflow.

Pre-Submit Review Prompt: “Review my current changes before I submit a pull request. Focus on: must-fix issues only. I want to know about critical bugs, security issues, and breaking changes. Do not spend time on style issues — our linter handles those. Provide your output as a numbered list of must-fix items with file, line, and specific fix recommendation.”

Post-Submit Review Prompt: “A pull request has been submitted for the following changes: [describe]. Review it from the perspective of a thorough senior developer. Provide: a summary of what the PR does, the top 5 issues you find most important (with severity and fix), suggestions for improving test coverage, and any architectural concerns.”

Expedited Review Prompt: “This is a small, low-risk PR: [describe change — e.g., documentation update, test addition, minor bug fix in well-tested code]. Give me a focused review that confirms the change is correct and safe, identifies any must-fix issues, and does not spend time on improvements that can be addressed later.”

8. Managing Review Quality

Cursor review quality improves over time when you refine your prompts based on feedback.

False Positive Audit Prompt: “In the last 30 days, Cursor review flagged these issues that turned out to be false positives: [list]. What patterns do these false positives share? How should I refine my review prompts to reduce false positives while maintaining sensitivity to real issues?”

Missed Issue Analysis Prompt: “A bug was discovered in production that was introduced in this PR: [describe the bug and the PR]. Would Cursor review have caught this if a focused review prompt had been used? What specific review focus would have caught it? Generate a new prompt template that would reliably catch this type of issue.”

FAQ

What is the best way to use Cursor review in a busy team workflow? Use Cursor for pre-submit review of every PR as a first pass — this catches must-fix issues before the PR enters the review queue. Then use focused human review on the items Cursor identifies as most significant. This two-stage approach improves review quality while reducing senior developer time per PR.

How do I get Cursor to respect my team’s specific coding standards? Include your coding standards explicitly in the review prompt. Create a stored/reusable prompt template that includes your conventions. Over time, refine the template based on what Cursor misses or incorrectly flags. The more specific and concrete your standards, the more accurately Cursor applies them.

What types of issues does Cursor review miss most often? Business logic errors (Cursor cannot evaluate whether the code does what the product intended), novel issues not in its training data, security issues that require understanding the full threat model, and issues where the correct behavior depends on context Cursor does not have access to.

Should I use Cursor review for all PRs or prioritize certain types? Use Cursor for all PRs as a first pass. Prioritize human review intensity based on risk: high-risk PRs (security changes, architectural changes, critical path code) get full human review. Low-risk PRs (documentation, simple refactoring, small bug fixes) can be reviewed primarily by Cursor with human approval.

Conclusion

Cursor review is most effective as a systematic first-pass reviewer that catches routine issues and surfaces the findings for human review. This two-stage model — AI review for the systematic, pattern-matching work, human review for the judgment-required work — improves both review quality and team velocity.

Your next step is to run Cursor pre-submit review on your next 10 pull requests using the Pre-Submit Review Prompt in this guide. Track the must-fix items it finds, compare them to what human reviewers find, and refine your review prompts based on the patterns you observe.