GPTPrompts.AI
ChatGPT for Code Generation
Write, debug, refactor, and optimize code faster with ChatGPT. From boilerplate to edge cases, master developer prompting.
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ChatGPT for Code Generation Overview
ChatGPT code generation prompts help developers write, debug, refactor, and optimize across languages and frameworks faster. Engineers use these programming prompts to bootstrap boilerplate, solve edge cases, document APIs, and conduct automated code reviews.
Key Capabilities:
- ✓ Function/method generation with full test suites
- ✓ API endpoint creation with validation
- ✓ Error diagnosis and root cause analysis
- ✓ Performance optimization and benchmarking
- ✓ Code refactoring for SOLID principles
- ✓ Microservice decomposition
- ✓ Auto-documentation generation
- ✓ Design pattern application
- ✓ Code review simulation
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Writing Code with ChatGPT
Function/Method Generator
Write [LANGUAGE] function: [NAME] Requirements: - Input: [PARAMETERS + TYPES + VALIDATION] - Output: [RETURN TYPE + FORMAT] - Edge cases to handle: [LIST 3-5] - Performance: [TIME/SPACE CONSTRAINTS] - Error handling: [STRATEGY] - Tests: Include 5 unit tests covering happy path + edges Follow [STYLE GUIDE/CONVENTIONS]. Add comprehensive docstring.
API Endpoint Builder
Create REST API endpoint in [FRAMEWORK: FastAPI/Express/Spring]: Endpoint: [METHOD] /api/[PATH] Auth: [SCHEME] Request body: [SCHEMA] Response: [SCHEMA + STATUS CODES] Database: [ORM/MODEL RELATIONS] Validation: [BUSINESS RULES] Logging: Structured + errors Security: Rate limiting + injection protection Include OpenAPI spec snippet.
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Debugging and Error Resolution
Error Trace Analyzer
Debug this error: [STACK TRACE/ERROR MESSAGE] Context: Language/framework: [DETAILS] Recent changes: [WHAT YOU CHANGED] Expected vs actual: [BEHAVIOR] Analysis: 1. Root cause hypothesis (ranked) 2. Reproduction steps 3. Fix code with explanation 4. Prevention: Linting rule or test to add
Performance Bottleneck Hunter
Optimize this [LANGUAGE] code: [PASTE CODE] Metrics: [CURRENT PERF, TARGET] Constraints: [MEMORY/CPU/BATTERY] Input size range: [SCALE] Deliver: BEFORE/AFTER benchmarks expected Algorithmic improvement explanation Complexity analysis (time/space) Production monitoring recommendations
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Refactoring and Architecture
Code Refactoring Transformer
Refactor this [LANGUAGE] code for [PRINCIPLE: SOLID/DRY/performance]: [PASTE CODE] New requirements: - Test coverage: 90%+ - Readability: Junior dev comprehensible - Extensibility: [FUTURE USE CASES] - Error boundaries: Graceful degradation Output: Refactored code + migration guide + before/after test suite.
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