Using Claude 3.5 Sonnet for Advanced Code Generation

Claude 3.5 Sonnet scored 92% on HumanEval, Anthropic's highest coding benchmark yet. We spent two weeks testing it on real development work to see if the benchmarks translate to practical improvements. Here's what actually works and where it falls short. ## What Changed in 3.5 Sonnet The coding improvements over Claude 3 Sonnet are substantial: **Code Generation** - 64% on HumanEval (vs 38.4% in Claude 3 Sonnet) - Better at translating natural language to code - Improved handling of edge cases **Debugging** - More accurate root cause identification - Better at tracing issues through multiple files - Improved error message interpretation **Code Understanding** - Better architecture comprehension - More accurate code explanations - Improved refactoring suggestions **Language Support** - Strong: Python, JavaScript/TypeScript, Go, Rust - Good: Java, C++, Ruby, Swift - Adequate: Most other mainstream languages ## Real Development Tasks We tested Claude 3.5 Sonnet on typical development work: **Task 1: Generate API Client** **Prompt**: "Create a Python client for the GitHub API that handles authentication, rate limiting, and common operations (repos, issues, PRs). Include error handling and retry logic." **Result**: Generated a functional 200-line client with proper class structure, error handling, and exponential backoff for rate limits. Required minor adjustments to import statements. **Time**: 30 seconds to generate, 5 minutes to test and adjust. **Verdict**: Would have taken 2-3 hours to write from scratch. Claude saved 90% of the work. **Task 2: Debug Production Issue** **Context**: Python service throwing intermittent "NoneType has no attribute" errors. Provided stack trace and relevant code. **Result**: Claude identified the issue in under 10 seconds - a race condition where async database call occasionally returned None before validation. Suggested adding explicit None check and proper async handling. **Verdict**: Found the issue faster than manual debugging. The suggested fix was correct. **Task 3: Refactor Legacy Code** **Prompt**: Provided 500-line JavaScript file with nested callbacks and asked to refactor to async/await with proper error handling. **Result**: Claude refactored the entire file, converting callbacks to async/await, adding try/catch blocks, and improving variable naming. Output was cleaner and more maintainable. **Edge case**: One complex callback pattern was converted incorrectly. Required manual fix. **Verdict**: 95% successful. Still saved hours of manual refactoring work. **Task 4: Write Tests** **Prompt**: "Write pytest tests for this database model class. Cover: CRUD operations, validation, edge cases, and database constraints." **Result**: Generated comprehensive test suite with 15 test cases, proper fixtures, and good edge case coverage. Tests passed on first run. **Verdict**: Excellent. Test writing is one of Claude's strongest use cases. **Task 5: Implement New Feature** **Prompt**: "Add rate limiting to this Flask API. Use Redis for distributed rate limiting. Support different limits per endpoint and user tier." **Result**: Claude generated a decorator-based rate limiting system with Redis backend, proper configuration, and different limit tiers. Architecture was sound, implementation needed minor adjustments for our specific Redis setup. **Verdict**: Good architectural approach. Required customization but saved significant development time. ## Claude 3.5 Sonnet vs GitHub Copilot We compared Claude to GitHub Copilot on the same tasks: **Autocomplete Speed** - Copilot: Instant inline suggestions - Claude: 5-15 second response time - **Winner: Copilot** (much faster for simple completions) **Code Understanding** - Copilot: Single-file context - Claude: Can analyze entire codebase (within token limits) - **Winner: Claude** (better architectural awareness) **Complex Refactoring** - Copilot: Struggles with multi-file changes - Claude: Handles cross-file refactoring well - **Winner: Claude** **Debugging** - Copilot: Limited debugging capabilities - Claude: Strong at root cause analysis - **Winner: Claude** **Boilerplate Generation** - Copilot: Excellent, minimal effort - Claude: Good but slower - **Winner: Copilot** (speed matters for simple tasks) **Integration** - Copilot: Native IDE integration - Claude: Separate interface (web or desktop app) - **Winner: Copilot** (better workflow integration) ## Best Practices **Provide Context** Claude performs better with more context. Upload related files, describe the architecture, explain requirements clearly. **Iterate** First generation is usually 80-90% correct. Review, identify issues, ask Claude to fix specific problems. **Test Everything** Never deploy Claude-generated code without testing. It makes subtle mistakes in complex scenarios. **Use for Scaffolding** Claude excels at creating initial structure. Let it generate the boilerplate, then customize to your needs. **Specific Prompts** Vague prompts produce generic code. Specific requirements, architectural preferences, and constraints produce better results. **Example: Bad Prompt** "Write a function to process user data" **Example: Good Prompt** "Write a Python function that takes a list of user dictionaries, validates email format, filters out inactive users (status != 'active'), and returns sorted by created_at timestamp. Include type hints and handle missing keys gracefully." ## Where It Still Struggles **Complex Domain Logic** Business-specific rules and complex domain logic require human judgment. Claude can structure it, but you need to provide the specifics. **Performance Optimization** Claude writes functional code but doesn't optimize for performance without explicit instruction. **Security** Works well when prompted for security considerations but doesn't proactively identify all vulnerabilities. Always review for security implications. **Unfamiliar Frameworks** Performance drops significantly with less common frameworks or very new libraries. ## Quick Takeaway Claude 3.5 Sonnet is excellent for code generation, debugging, and refactoring. It works best as a pair programming partner - providing structure and handling boilerplate while you focus on architecture and domain logic. It's slower than GitHub Copilot for autocomplete but significantly better for complex tasks like multi-file refactoring, architectural decisions, and debugging. Use Copilot for fast inline suggestions, Claude for complex development work.