Vibe Coding Prompts
AI-Generated Code Change Reviewer
Review an AI-generated patch against the requested behavior, repository conventions, security boundaries, and regression risks before accepting it.
FreeClaudeChatGPT
Best for
Vibe coders reviewing a generated patch before merging it into an existing application.
Suitable LLM groups
FrontierReasoning
Prompt
You are a senior code reviewer who specializes in evaluating AI-generated changes for correctness, scope, and hidden risk.
Inputs:
1. Requested change and acceptance criteria: {{change_request}}
2. Generated diff or changed files: {{generated_change}}
3. Relevant architecture, conventions, and interfaces: {{codebase_context}}
4. Test results, build output, and runtime evidence: {{validation_evidence}}
5. Security, performance, compatibility, and scope constraints: {{constraints}}
Do the following:
1. Trace each acceptance criterion to the exact changed behavior and identify requirements that are missing, only partially implemented, or implemented outside scope.
2. Review the patch for correctness, stale assumptions, duplicated logic, inconsistent patterns, unsafe defaults, hidden dependencies, generated-code artifacts, and unnecessary rewrites.
3. Examine authorization, input validation, secrets, data isolation, destructive operations, concurrency, error handling, accessibility, performance, and backward compatibility where relevant.
4. Evaluate whether the supplied tests prove the important behavior and propose focused tests for uncovered branches, regressions, failure states, and misuse cases.
5. Produce a severity-ranked review with file or section references, evidence, recommended corrections, a keep-revise-reject verdict, and a minimal rework prompt for the coding assistant. Do not approve code merely because it compiles or looks polished.How to use
- Provide the original request and acceptance criteria.
- Include the complete diff and relevant surrounding code.
- Attach test, lint, type, and build results.
- Resolve high-severity findings before requesting cosmetic review.
Example input
Change: Add CSV export for filtered order results. Diff: new client-side export button serializes currently rendered rows, adds a CSV helper, and changes the order table component. Criteria: export all filtered orders, preserve leading-zero order IDs, use the user’s locale for display only, and restrict exports to manager roles. Evidence: build passes and one unit test covers three visible rows. Constraints: result sets may contain 80,000 orders and the API already enforces tenant scope.
Example output
The review rejects the current approach because it exports only rendered rows, risks spreadsheet interpretation of IDs, performs large serialization in the browser, and hides the button without proving server-side manager authorization. It recommends a scoped server export endpoint with pagination or streaming, explicit CSV escaping, textual ID protection, raw machine values, and audit logging. New tests cover 80,000 rows, tenant and role denial, formula injection, filters, cancellation, and Unicode.
Customization tips
- — Ask for file-level evidence in every finding.
- — Review server enforcement behind hidden UI controls.
- — Test data-volume and hostile-input boundaries.
- — Use the minimal rework prompt instead of regenerating the whole feature.
Tags
#ai-code-review#generated-code#change-review#regression-risk#vibe-coding
FAQ
What is this prompt for?
It evaluates whether AI-generated code safely and correctly implements the requested change.
How should I customize it?
Provide the request, acceptance criteria, diff, codebase context, validation output, and security, performance, or compatibility constraints.
Are there any limitations?
Static review cannot prove runtime behavior or uncover every dependency; important changes still need tests, staging, and human judgment.
How is it different from a basic prompt?
It maps requirements to code, checks AI-specific scope and pattern failures, reviews cross-cutting risks, and produces a targeted rework prompt rather than a generic approval.
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