# Claude 3.5 Sonnet

## Overview
Claude 3.5 Sonnet is Anthropic's mid-tier model with strong coding capabilities, particularly good at understanding context and following complex instructions.

## Strengths

### Code Understanding
- Excellent at understanding large codebases when given sufficient context
- Strong ability to trace logic through multiple files
- Good at identifying architectural patterns

### Code Generation
- Produces clean, well-structured code
- Follows coding conventions consistently when specified
- Strong at multiple programming languages (Python, JavaScript/TypeScript, Java, Go, Rust)
- Good at generating tests alongside implementation

### Refactoring
- Excels at refactoring while maintaining functionality
- Can explain trade-offs between different approaches
- Good at modernizing legacy code

### Documentation
- Generates comprehensive, useful documentation
- Good at explaining complex concepts clearly
- Creates helpful code comments when appropriate

## Limitations

### Context Window
- 200K token context window (as of late 2024)
- Can handle large files but may need chunking for very large codebases

### Real-time Information
- Knowledge cutoff (check current date)
- Cannot access external APIs or live documentation without tools

### Specialized Domains
- May not know very new frameworks or libraries released after training
- Less familiar with highly specialized or proprietary technologies

## Prompting Style

### What Works Well

**Be Explicit About Requirements**
```
Good: "Create a React component using functional components with hooks. 
Use TypeScript. Follow our naming convention: PascalCase for components."

Less effective: "Create a React component"
```

**Provide Context**
```
Good: "Here's our existing authentication pattern: [paste code]. 
Create a new endpoint following this same pattern."

Less effective: "Create an authentication endpoint"
```

**Ask for Explanations**
- Claude excels at explaining reasoning
- Use "explain why you chose this approach" for learning
- Request alternatives: "what are other ways to solve this?"

**Iterative Refinement**
- Start high-level, then drill down
- "Good start, now add error handling for [specific cases]"
- Build on previous responses in conversation

### What to Avoid

**Vague Requests**
- "Make it better" → Specify what aspect to improve
- "Fix this" → Describe what's wrong

**Assuming Context**
- Don't assume Claude remembers previous conversations (unless in same session)
- Re-provide key context when switching topics

## Quirks

### Helpful but Sometimes Overcautious
- May add more error handling than strictly necessary
- Sometimes over-explains basic concepts
- Can be overly verbose in documentation

**Workaround**: 
- Specify "concise" or "minimal comments" if preferred
- Ask for "production-ready code without excessive comments"

### Strong Preference for Best Practices
- Tends toward modern, idiomatic code
- May suggest improvements even when not asked

**Workaround**:
- Specify if you need legacy-compatible code
- Mention constraints explicitly

### Careful with Security
- Very cautious about security implications
- Will point out potential vulnerabilities

**This is a feature**: Leverage this for security reviews

## Best Use Cases

### Ideal For
1. **Feature Implementation**: Complex features requiring multiple components
2. **Code Review**: Identifying issues and suggesting improvements
3. **Refactoring**: Modernizing code while maintaining functionality
4. **Learning**: Understanding new concepts or frameworks
5. **Documentation**: Generating comprehensive docs
6. **Test Generation**: Creating thorough test suites
7. **Debugging**: Analyzing and fixing complex bugs

### Less Ideal For
1. **Quick Snippets**: May be overly thorough for simple tasks (but still good)
2. **Cutting-edge Frameworks**: If released after knowledge cutoff
3. **Domain-specific Tasks**: Highly specialized domains might need expert review

## Tips for Maximum Effectiveness

### 1. Conversation Structure
- Start with overview of what you're building
- Provide relevant code snippets
- Build iteratively

### 2. Use Artifacts
- Claude will use artifacts for substantial code
- Review artifacts carefully before copying

### 3. Ask for Multiple Options
- "Show me 2-3 approaches for this problem"
- Compare trade-offs

### 4. Request Self-Review
- After generation: "Review this code for security issues"
- "What edge cases might this code not handle?"

### 5. Leverage Strengths
- Use for architectural guidance
- Ask about best practices
- Request explanations to learn

## Example Workflows

### Workflow 1: Bug Investigation
```
1. "Here's a bug I'm seeing: [description]"
2. [Share relevant code]
3. "What could be causing this?"
4. [Claude analyzes and suggests causes]
5. "Let's try your second suggestion. Show me the fix."
6. [Implement and verify]
```

### Workflow 2: Feature Implementation
```
1. "I need to build [feature]. Here's my tech stack: [details]"
2. [Claude provides plan]
3. "Let's start with step 1. Here's our existing pattern: [code]"
4. [Iterate through implementation]
5. "Now generate tests for this"
6. [Review and integrate]
```

## Version-Specific Notes

**Claude 3.5 Sonnet** (current as of documentation)
- Released: June 2024
- Strong improvements in coding over previous versions
- Better at following complex, multi-step instructions
- Improved at maintaining context through long conversations

## Comparison Notes

**vs GPT-4**: 
- Often more thorough in explanations
- Similar code quality
- Different "personality" - can try both and see preference

**vs GitHub Copilot**:
- Claude better for complex refactoring and architecture
- Copilot better for in-editor autocomplete
- Use both: Copilot for coding, Claude for planning and review

**vs GPT-3.5**:
- Significantly better at complex coding tasks
- Much better context retention
- More reliable outputs

## Last Updated
Add update date when you modify this document based on new findings.
