Prompts Library — By Role

AI Prompts for Developers

Accelerate code reviews, squash bugs faster, and write documentation that teams actually read. These prompts are built for real engineering workflows.

6 Essential Developer Prompts

Review this code for bugs, security vulnerabilities, performance issues, and readability. Language: [LANGUAGE]. Context: this function/module handles [DESCRIBE PURPOSE]. Flag issues by severity (critical, warning, suggestion). For each issue: explain the problem, show the problematic line, and provide a corrected version. Also note any missing error handling or edge cases. Code: [PASTE CODE]

I am getting this error and cannot figure out the cause. Language/Framework: [LANGUAGE/FRAMEWORK]. Error message: “[PASTE ERROR]”. Relevant code: “[PASTE CODE]”. What I have already tried: [LIST ATTEMPTS]. Explain the root cause step by step, provide a fix with code, and suggest how to prevent this type of error in the future. If there are multiple possible causes, list them in order of likelihood.

Write comprehensive API documentation for this endpoint. Method: [GET/POST/PUT/DELETE]. Path: [/api/v1/resource]. Purpose: [DESCRIBE]. Include: description, authentication requirements, request parameters (path, query, body) with types and validation rules, response schema with example JSON for success (200) and common errors (400, 401, 404), rate limits if applicable, and a curl example. Follow OpenAPI/Swagger conventions.

Write unit tests for this function using [TESTING FRAMEWORK, e.g., Jest, pytest, JUnit]. Cover: happy path, edge cases (empty input, null values, boundary values), error conditions, and any important business logic branches. Each test should have a descriptive name explaining what it verifies. Target: [NUMBER]% code coverage. Function to test: [PASTE FUNCTION]

Refactor this code to improve readability, reduce complexity, and follow [LANGUAGE] best practices. Current complexity concerns: [DESCRIBE, e.g., deeply nested conditionals, repeated logic, unclear naming]. Preserve the exact same functionality and return values. Explain each refactoring decision. If you split into multiple functions, explain the single-responsibility principle behind each. Code: [PASTE CODE]

Create a README.md for this project. Repository: [REPO NAME]. Tech stack: [LIST TECHNOLOGIES]. Include: project description (2-3 sentences), features list, prerequisites, installation steps (exact commands), environment variable setup (.env.example format), how to run locally, how to run tests, project structure overview (folder tree), contributing guidelines, and license. Keep it concise but complete enough for a new developer to get started in under 10 minutes.

Frequently Asked Questions

Should I trust AI-generated code in production?

Never blindly copy AI-generated code into production. Always review it for correctness, security, and edge cases. AI is excellent at scaffolding, boilerplate, and suggesting approaches, but a human developer must verify the output just as they would review a junior developer’s pull request.

Which AI model is best for coding tasks?

Claude excels at explaining complex code and reasoning through architecture decisions. GPT-4 is strong at code generation across many languages. GitHub Copilot is best for inline autocomplete during active coding. Use the right tool for the right task.

How do I give AI enough context for accurate code help?

Include the language, framework version, the purpose of the code, relevant type definitions or interfaces, and what you have already tried. The more specific your context, the more accurate the output. Pasting the actual error message and stack trace is far better than describing the problem vaguely.