# Playbook: Implementing a New Feature with AI Assistance

## Overview
A step-by-step guide for implementing new features using AI-assisted development, from planning through deployment.

## Prerequisites
- Access to AI coding assistant (Claude, Copilot, etc.)
- Understanding of the feature requirements
- Familiarity with your codebase
- Development environment set up

## Steps

### 1. Understand and Plan

**Goal**: Clearly define what you're building and break it into manageable pieces

**Action**: 
- Review feature requirements thoroughly
- Identify affected components
- Sketch out data flow and architecture

**AI Prompt Template**:
```
I need to implement [feature description]. Here's what it needs to do:
[List requirements]

My tech stack is: [languages, frameworks, databases]

Can you help me break this down into implementation steps and identify potential challenges?
```

**Expected Output**: Structured implementation plan with potential gotchas

**Tips**: 
- Don't skip this step - planning saves debugging time later
- Ask AI about edge cases you might have missed
- Validate the plan against your existing architecture

### 2. Create Data Models/Interfaces

**Goal**: Define the data structures and contracts first

**Action**: Start with types, interfaces, or data models

**AI Prompt Template**:
```
Based on these requirements: [paste requirements]

Generate [TypeScript interfaces / Python models / Java classes] for:
- [Entity 1]
- [Entity 2]
- Any DTOs needed for API communication

Follow our existing pattern in [example file/directory].
```

**Expected Output**: Type-safe data structures with proper documentation

**Tips**:
- Review generated types carefully
- Ensure they align with existing naming conventions
- Consider validation requirements

### 3. Implement Core Logic

**Goal**: Build the main functionality with AI assistance

**Action**: Work on core business logic, one component at a time

**AI Prompt Template**:
```
I need to implement [specific function/class] that [description].

Context:
- It will use [dependencies/services]
- Input: [describe input]
- Output: [describe output]
- Edge cases: [list edge cases]

Existing pattern example: [paste relevant existing code]

Please implement this following our conventions.
```

**Expected Output**: Implementation that matches your codebase style

**Tips**:
- Start with the happy path
- Add error handling in iteration
- Request explanations for complex logic
- Don't implement everything at once

### 4. Add Error Handling and Validation

**Goal**: Make the code robust and production-ready

**Action**: Add comprehensive error handling

**AI Prompt Template**:
```
Here's my implementation: [paste code]

Please add:
1. Input validation
2. Error handling for [specific scenarios]
3. Logging at appropriate points
4. Any defensive checks needed

Follow error handling patterns from: [example file]
```

**Expected Output**: Hardened code with proper error handling

**Tips**:
- Be specific about error scenarios
- Consider both expected and unexpected errors
- Don't over-engineer for unlikely scenarios

### 5. Write Tests

**Goal**: Ensure code works correctly and prevent regressions

**Action**: Create comprehensive test coverage

**AI Prompt Template**:
```
Create tests for this implementation: [paste code]

Test framework: [Jest/pytest/JUnit/etc.]
Test style: [unit/integration/both]

Cover:
- Happy path
- Edge cases: [list specific cases]
- Error scenarios: [list error cases]
- [Any specific test requirements]
```

**Expected Output**: Complete test suite

**Tips**:
- Review tests for completeness
- Ensure tests are independent
- Check that tests actually validate behavior
- Run tests to verify they pass

### 6. Review and Refine

**Goal**: Ensure code quality before integration

**Action**: Critical review of all AI-generated code

**AI Prompt Template**:
```
Review this code for:
- Security vulnerabilities
- Performance issues
- Best practices violations
- Maintainability concerns

Code: [paste implementation]

Our standards: [mention specific standards]
```

**Expected Output**: List of improvements and potential issues

**Tips**:
- Don't skip manual review
- Test the actual feature in your app
- Check for security implications
- Verify it fits with existing code

### 7. Document

**Goal**: Make the feature understandable for future developers

**Action**: Add necessary documentation

**AI Prompt Template**:
```
Generate documentation for: [paste code]

Include:
- Function/class documentation
- Usage examples
- Important notes or gotchas
- [README updates if needed]

Style: [JSDoc/docstrings/Javadoc/etc.]
```

**Expected Output**: Clear, useful documentation

**Tips**:
- Documentation should explain "why", not just "what"
- Include examples for complex features
- Update README or wiki if needed

### 8. Integration Testing

**Goal**: Verify the feature works in the full system

**Action**: Test in realistic environment

- Run full test suite
- Manual testing of the feature
- Check integration points
- Verify no regressions

**Tips**:
- Test with realistic data
- Check performance under load if relevant
- Verify error messages are user-friendly

## Success Criteria

- [ ] Feature meets all requirements
- [ ] Tests pass and provide good coverage
- [ ] Code reviewed and follows standards
- [ ] Documentation complete
- [ ] No regressions in existing functionality
- [ ] Security validated
- [ ] Performance acceptable

## Troubleshooting

**Problem**: AI generates code that doesn't match existing patterns
- **Solution**: Provide more context and examples from existing code

**Problem**: Tests are passing but feature doesn't work
- **Solution**: Review test quality, ensure they test actual behavior

**Problem**: Generated code has security issues
- **Solution**: Explicitly ask AI to review for security, manually audit

**Problem**: Code is overly complex
- **Solution**: Ask AI to simplify, request alternative approaches

## Next Steps

After successful implementation:
1. Create pull request with detailed description
2. Address code review feedback
3. Monitor feature after deployment
4. Document any learnings for future reference
