Code Generation with Gemini
Gemini 2.5 Pro excels at multi-file code generation. Specifying the framework, existing patterns, and output structure produces code that fits into your project rather than generic examples.
Generate a {{language}} implementation for {{feature_description}}. Project context: - Framework: {{framework}} - Existing patterns: {{pattern_description}} - This code will be used in: {{usage_context}} Requirements: 1. Follow {{framework}} best practices and idioms 2. Include comprehensive error handling with typed errors 3. Add TypeScript types / type hints for all parameters and return values 4. Write the code in a way that's testable (dependency injection where appropriate) 5. Include inline comments ONLY for non-obvious logic Output format: - Main implementation file - Types/interfaces file (if needed) - One example usage showing how to call this code Do not include package installation instructions or boilerplate I didn't ask for.
Variables to customize
Why this prompt works
Gemini 2.5 Pro excels at multi-file code generation. Specifying the framework, existing patterns, and output structure produces code that fits into your project rather than generic examples.
Save this prompt to your library
Organize, version, and access your best prompts across ChatGPT, Claude, and Cursor.
Related prompts
Forcing the agent to plan before acting prevents premature execution and wasted steps. Explicit dependency mapping enables parallel execution and catches logical gaps early.
Tool Selection AgentThe ReAct pattern (Reason + Act) creates an explicit reasoning trace that improves tool selection accuracy. The error-handling rule prevents infinite retry loops.
Prompt CompressorExplicitly requiring all functional requirements to be preserved prevents the model from over-compressing and losing critical instructions.
Memory Management AgentExplicit memory read/write instructions create agents that improve over time. Categorization keeps memories organized, and the deduplication rule prevents context bloat.