💻 Coding & Development
Code Reviewer
Example outputs
Here is what ChatGPT, Claude, and Gemini typically return for this prompt:
I'd be happy to help review your code! Please share the code block, method, or file you'd like me to review, along with the programming language name. I'll analyze it for potential issues, suggest improvements, and provide alternative approaches where applicable, complete with detailed explanations for each recommendation.
I'm ready to review your code. Please provide the code snippet or file you'd like me to examine, and let me know which programming language it's written in. I'll offer constructive feedback on code quality, potential bugs, and suggest alternatives where appropriate, though I should note my suggestions will be based on general best practices rather than knowledge of your specific project context.
Please provide the code block, method, or file you want reviewed, along with the programming language. I'll analyze it for correctness, performance, maintainability, and security concerns, then provide specific feedback with explanations and alternative implementation approaches based on established coding standards and best practices.
How to use this prompt
Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.
Which AI model works best
Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.
How to customize this prompt
Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).
Common use cases
- Writing production code with strict style requirements
- Reviewing pull requests and catching bugs before merge
- Converting between languages (Python → TypeScript, for example)
- Generating unit tests for existing functions
- Explaining unfamiliar codebases to new team members
Variations
Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).