# PRD: MCP AI Agent System Prompt Optimization
**GitHub Issue**: [#36](https://github.com/vfarcic/dot-ai/issues/36)
**Created**: 2025-07-27
**Status**: Planning
**Priority**: Medium
## 1. Problem Statement
The DevOps AI Toolkit's MCP AI agent currently uses a default system prompt that may not be optimally tailored for DevOps and Kubernetes deployment scenarios. This could result in:
- Suboptimal recommendation quality for Kubernetes deployments
- Inconsistent responses across different use cases
- Missed opportunities to leverage domain-specific knowledge
- Reduced user satisfaction and trust in AI recommendations
## 2. Success Metrics
### Primary Metrics
- **Recommendation Quality**: Improved user feedback scores for AI-generated solutions
- **Response Consistency**: Reduced variance in recommendation quality across similar scenarios
- **Domain Alignment**: Better integration of DevOps/Kubernetes best practices in responses
### Secondary Metrics
- **User Engagement**: Increased usage of AI recommendation features
- **Development Velocity**: Reduced time spent on prompt engineering during feature development
- **Maintainability**: Clearer separation of concerns between AI logic and domain expertise
## 3. User Stories
### Primary Users: DevOps Engineers & Platform Teams
- **As a DevOps engineer**, I want AI recommendations that understand my infrastructure constraints and follow industry best practices
- **As a platform team member**, I want consistent, reliable AI suggestions that align with our organizational standards
- **As a new user**, I want AI responses that help me learn DevOps concepts while solving immediate problems
### Secondary Users: Development Team
- **As a developer**, I want the AI system prompt to be configurable and testable for different scenarios
- **As a maintainer**, I want clear documentation on how system prompts affect AI behavior
## 4. Requirements
### Functional Requirements
- **FR1**: Research current system prompt effectiveness through user feedback analysis
- **FR2**: Design configurable system prompt architecture for testing variations
- **FR3**: Create domain-specific prompt templates for different DevOps scenarios
- **FR4**: Implement A/B testing capability for prompt optimization
- **FR5**: Document optimal system prompt configurations and usage guidelines
### Non-Functional Requirements
- **NFR1**: Maintain backward compatibility with existing MCP integration
- **NFR2**: Ensure prompt changes don't negatively impact response time
- **NFR3**: Support easy rollback of prompt modifications
- **NFR4**: Enable monitoring and logging of prompt effectiveness
## 5. Solution Architecture
### Research Phase
- Analyze current AI agent responses across different DevOps scenarios
- Gather user feedback on recommendation quality and relevance
- Study industry best practices for AI prompt engineering in DevOps contexts
### Design Phase
- Create configurable prompt system with environment-specific variations
- Design testing framework for prompt effectiveness measurement
- Establish metrics for evaluating prompt performance
### Implementation Phase
- Build prompt configuration management system
- Implement A/B testing infrastructure
- Create monitoring and feedback collection mechanisms
## 6. Implementation Plan
### Milestone 1: Research & Analysis Foundation ✅
**Goal**: Understand current state and identify optimization opportunities
- [ ] Analyze existing system prompt configuration
- [ ] Document current AI response patterns and quality
- [ ] Research DevOps-specific prompt engineering best practices
- [ ] Create evaluation framework for prompt effectiveness
### Milestone 2: Configurable Prompt Architecture
**Goal**: Build infrastructure for prompt experimentation and testing
- [ ] Design configurable system prompt architecture
- [ ] Implement environment-specific prompt loading
- [ ] Create A/B testing framework for prompt variations
- [ ] Build monitoring and metrics collection system
### Milestone 3: Domain-Optimized Prompts
**Goal**: Develop and test DevOps-specific prompt configurations
- [ ] Create specialized prompts for different DevOps scenarios
- [ ] Test prompt variations against real-world use cases
- [ ] Optimize prompts based on performance metrics
- [ ] Document optimal configurations and usage patterns
### Milestone 4: Production Integration & Validation
**Goal**: Deploy optimized prompts with monitoring and feedback loops
- [ ] Implement production-ready prompt management system
- [ ] Deploy optimized prompts with gradual rollout
- [ ] Monitor impact on user satisfaction and recommendation quality
- [ ] Create documentation and guidelines for ongoing optimization
### Milestone 5: Documentation & Knowledge Transfer
**Goal**: Complete feature documentation and enable team adoption
- [ ] Document system prompt optimization methodology
- [ ] Create user guides for prompt configuration
- [ ] Train team on prompt management and optimization
- [ ] Establish processes for ongoing prompt maintenance
## 7. Technical Considerations
### System Integration
- Integrate with existing MCP server architecture
- Maintain compatibility with Claude AI integration patterns
- Support for multiple prompt configurations and switching
### Testing Strategy
- Unit tests for prompt configuration loading
- Integration tests for different prompt scenarios
- A/B testing framework for measuring prompt effectiveness
- User acceptance testing with domain experts
### Monitoring & Observability
- Track prompt usage patterns and effectiveness metrics
- Monitor AI response quality and user satisfaction
- Alert on prompt-related performance degradation
- Dashboard for prompt performance analytics
## 8. Risks & Mitigation
| Risk | Impact | Probability | Mitigation Strategy |
|------|--------|-------------|-------------------|
| Prompt changes degrade AI quality | High | Medium | Comprehensive A/B testing, gradual rollout |
| Complex configuration increases maintenance burden | Medium | High | Keep configuration simple, good documentation |
| User expectations not met | Medium | Medium | Continuous feedback collection, iterative improvement |
| Performance impact from prompt processing | Low | Low | Optimize prompt loading, caching strategies |
## 9. Dependencies
### Internal Dependencies
- Claude AI integration (`src/core/claude.ts`)
- MCP server implementation (`src/mcp/server.ts`)
- Existing prompt loading system (`prompts/` directory)
### External Dependencies
- Anthropic Claude API compatibility
- MCP protocol requirements
- User feedback collection mechanisms
## 10. Success Criteria
### Must-Have (Launch Requirements)
- [ ] Configurable system prompt architecture implemented
- [ ] At least 20% improvement in user-reported recommendation quality
- [ ] Zero degradation in AI response time or availability
- [ ] Complete documentation for prompt configuration and optimization
### Should-Have (Post-Launch Goals)
- [ ] A/B testing framework for ongoing optimization
- [ ] Multiple domain-specific prompt templates
- [ ] Automated prompt performance monitoring
- [ ] Integration with user feedback systems
### Could-Have (Future Enhancements)
- [ ] Machine learning-based prompt optimization
- [ ] Dynamic prompt adaptation based on user context
- [ ] Community-contributed prompt templates
- [ ] Advanced analytics and reporting dashboard
## 11. Documentation Requirements
<!-- PRD-36 -->
All documentation will include traceability comments linking back to this PRD.
### New Documentation Files
- System prompt optimization guide
- Prompt configuration reference
- A/B testing methodology documentation
### Documentation Updates Required
- Update MCP setup guide with prompt configuration options
- Enhance API documentation with prompt management endpoints
- Add troubleshooting section for prompt-related issues
## 12. Progress Log
### 2025-07-27
- ✅ Created GitHub issue #36
- ✅ Created initial PRD structure
- 🔄 Beginning research and analysis phase
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**Next Steps**: Start with Milestone 1 research and analysis to understand current prompt effectiveness and identify specific optimization opportunities.
