# PRD: Advanced Memory Learning Algorithms for AI-Powered Deployments
**Created**: 2025-07-28
**Status**: Draft
**Owner**: Viktor Farcic
**Last Updated**: 2025-07-28
## Executive Summary
Implement sophisticated AI/ML algorithms that recognize patterns, track success rates, analyze configurations, and provide intelligent recommendations based on accumulated deployment knowledge.
## Documentation Changes
### Files Created/Updated
- **`docs/learning-algorithms-guide.md`** - New File - Complete guide for AI learning and pattern recognition features
- **`docs/advanced-features.md`** - Advanced Features - Add learning algorithms to AI capabilities
- **`docs/mcp-guide.md`** - MCP Documentation - Add pattern analysis and learning inspection MCP tools
- **`README.md`** - Project Overview - Add AI learning algorithms to core capabilities
- **`src/core/learning/`** - Technical Implementation - Advanced learning algorithm modules
### Content Location Map
- **Feature Overview**: See `docs/learning-algorithms-guide.md` (Section: "What are Learning Algorithms")
- **Pattern Recognition**: See `docs/learning-algorithms-guide.md` (Section: "Pattern Recognition Engine")
- **Success Tracking**: See `docs/learning-algorithms-guide.md` (Section: "Success Rate Analysis")
- **Setup Instructions**: See `docs/learning-algorithms-guide.md` (Section: "Configuration")
- **MCP Tools**: See `docs/mcp-guide.md` (Section: "Learning and Analysis Tools")
- **Examples**: See `docs/learning-algorithms-guide.md` (Section: "Usage Examples")
### User Journey Validation
- [ ] **Primary workflow** documented end-to-end: Deploy → System learns → Pattern analysis → Improved recommendations
- [ ] **Secondary workflows** have complete coverage: Pattern inspection, success analysis, learning insights
- [ ] **Cross-references** between basic AI features and advanced learning work correctly
- [ ] **Examples and commands** are testable via automated validation
## Implementation Requirements
- [ ] **Core functionality**: Pattern recognition algorithms for deployment similarity - Documented in `docs/learning-algorithms-guide.md` (Section: "Pattern Recognition")
- [ ] **User workflows**: Success rate tracking with optimization recommendations - Documented in `docs/learning-algorithms-guide.md` (Section: "Success Analysis")
- [ ] **MCP Tools**: Configuration effectiveness analysis and insights - Documented in `docs/mcp-guide.md` (Section: "Learning and Analysis Tools")
- [ ] **Performance optimization**: <200ms for pattern analysis operations
### Success Criteria
- [ ] **Pattern accuracy**: Recognition accuracy >85% for similar deployments
- [ ] **Success optimization**: Tracking provides actionable optimization recommendations
- [ ] **Configuration insights**: Analysis identifies effective deployment patterns
- [ ] **Recommendation improvement**: Intelligent recommendations improve deployment success rates by >20%
## Implementation Progress
### Phase 1: Pattern Recognition and Success Tracking [Status: ⏳ PENDING]
**Target**: Basic learning algorithms with pattern recognition working
**Implementation Tasks:**
- [ ] Design advanced memory schemas with pattern storage and analysis
- [ ] Implement pattern recognition algorithms for deployment similarity detection
- [ ] Build success rate tracking and correlation analysis system
- [ ] Create configuration effectiveness analysis module
### Phase 2: Intelligent Recommendation Engine [Status: ⏳ PENDING]
**Target**: AI-powered recommendations based on learned patterns
**Implementation Tasks:**
- [ ] Develop networking and access pattern storage capabilities
- [ ] Implement machine learning-inspired matching algorithms
- [ ] Build deployment success correlation analysis
- [ ] Create intelligent recommendation engine using historical data
### Phase 3: Advanced Learning Features [Status: ⏳ PENDING]
**Target**: Sophisticated learning with insights and optimization
**Implementation Tasks:**
- [ ] Add advanced pattern analysis and insights generation
- [ ] Implement performance optimization recommendations
- [ ] Create learning analytics and metrics dashboard
- [ ] Build continuous improvement algorithms
## Work Log
### 2025-07-28: PRD Refactoring to Documentation-First Format
**Completed Work**: Refactored PRD #7 to follow new documentation-first guidelines with comprehensive learning algorithm features mapped to user documentation.
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## Appendix
### Learning Algorithm Categories
- **Pattern Recognition**: Deployment similarity and configuration matching
- **Success Rate Analysis**: Historical outcome tracking and optimization
- **Configuration Analysis**: Effectiveness assessment and recommendations
- **Networking Patterns**: Access and connectivity learning
- **Resource Optimization**: Usage pattern analysis and right-sizing
