DevOps AI Toolkit

# PRD #83: Intent Clarification Enhancement - Remove Constraints for Comprehensive Questions **GitHub Issue**: [#83](https://github.com/vfarcic/dot-ai/issues/83) **Status**: ✅ **COMPLETE** (2024-08-30) **Priority**: High **Owner**: TBD ## Problem Statement The current intent clarification system constrains AI thinking with rigid categories and artificial limits, preventing comprehensive question generation for diverse deployment intents. This results in missing critical context that would significantly improve solution recommendations. **Current Broken Experience**: - **User Input**: "Create PostgreSQL database in AWS" - **System Response**: 5 generic questions from fixed categories (Technical Specifications, Architectural Context, etc.) - **Missing Questions**: Database schema, public accessibility, initial data requirements, backup strategy - **Root Cause**: AI forced to think in 5 predefined boxes instead of exploring what's actually missing **Impact**: - **Vague intents get too few questions** when they need the most exploration - **Critical domain-specific context missed** (schemas, APIs, business rules) - **Generic recommendations** that don't match actual requirements - **Artificial 5-question limit** prevents thorough exploration ## Success Criteria ### Primary Objectives 1. **Unconstrained Question Generation** - AI explores all relevant missing context without category limits 2. **Intent-Adaptive Questioning** - Vague intents get comprehensive exploration, specific intents get targeted questions 3. **Domain-Specific Intelligence** - Questions about schemas, APIs, business rules, infrastructure as needed 4. **User-Controlled Depth** - Users decide relevance by answering what matters to them 5. **Always Comprehensive** - Open-ended question ensures nothing is missed ### Success Metrics 1. **Question Relevance** - Questions match the specific deployment type and context 2. **Coverage Completeness** - All major configuration aspects explored for vague intents 3. **Flexibility** - Question count varies naturally based on intent specificity (3-15+ questions) 4. **User Satisfaction** - Better solution matches due to comprehensive context gathering 5. **No Missed Opportunities** - Critical requirements captured that would change solution approach ## Solution Approach ### Core Philosophy: Unconstrained Exploration Remove ALL artificial constraints and let AI intelligently determine what clarification is needed for each unique intent. ### Phase 1: Remove AI Constraints ✨ **Objective**: Transform the AI prompt to enable comprehensive, unconstrained question generation **Implementation Areas**: - **Remove Rigid Categories** - Eliminate the 5 fixed categories that force AI into boxes - **Remove Quantity Limits** - Remove "quality over quantity" and "prioritize ruthlessly" guidance - **Encourage Thoroughness** - Guide AI to explore every aspect that could improve solutions - **Pattern Recognition** - Let AI adapt questioning based on deployment type **Tasks**: - [x] **Remove categorical framework** from `prompts/intent-analysis.md` - [x] **Replace with exploration principles** - "Identify ALL information that would create perfect solution" - [x] **Add comprehensive guidance** - "Be thorough - explore every aspect that could influence deployment" - [x] **Simplify response structure** - Remove rigid category fields from JSON output - [x] **Add open-ended requirement** - Always include final catch-all question ### Phase 2: Remove Code Constraints 🔓 **Objective**: Remove all artificial limits in the recommendation tool implementation **Implementation Areas**: - **Remove Question Limits** - Remove `.slice(0, 5)` constraint in recommend tool - **Remove Impact Filtering** - Remove HIGH/MEDIUM filtering that reduces question count - **Pass Through All Questions** - Let AI's judgment determine optimal quantity - **Update Response Handling** - Support variable question counts **Tasks**: - [x] **Remove question slicing** in `src/tools/recommend.ts` - [x] **Remove impact level filtering** for question selection - [x] **Update response formatting** to handle variable counts - [x] **Simplify question structure** without rigid categorization ### Phase 3: Update Documentation 📚 **Objective**: Document the enhanced intent clarification process for users **Implementation Areas**: - **Primary Workflow Guide** - Complete documentation of clarification process - **Feature Descriptions** - Update main documentation to reflect enhancement - **Tool References** - Ensure consistent description across documentation **Tasks**: - [x] **Add clarification workflow section** to `docs/mcp-recommendation-guide.md` - [x] **Update feature descriptions** in `README.md` - [x] **Update tool descriptions** in `docs/mcp-tools-overview.md` - [x] **Add comprehensive examples** showing variable question counts - [x] **Document final parameter usage** for skipping clarification ### Phase 4: Update Test Coverage 🧪 **Objective**: Ensure tests validate the new unconstrained approach **Implementation Areas**: - **Variable Question Counts** - Tests should handle 3-15+ questions - **Flexible Response Structure** - Validate new JSON structure - **Quality Validation** - Ensure questions are relevant without rigid categories **Tasks**: - [x] **Update recommend tool tests** for variable question counts - [x] **Update intent analysis tests** for new prompt structure - [x] **Add comprehensive scenario tests** for different intent types - [x] **Validate open-ended question inclusion** in all responses ## Expected Outcomes ### Question Generation Examples **Vague Intent**: "Create PostgreSQL database in AWS" - **Before**: 5 generic questions from fixed categories - **After**: 10-12 comprehensive questions covering: - Database hosting approach (RDS vs. in-cluster vs. managed) - Schema requirements and initial data - Access patterns (public, private, VPN) - Performance and scaling needs - Backup and disaster recovery - Security and compliance requirements - Integration with other services - Monitoring and alerting - Cost considerations - Open-ended: "Any other specific requirements..." **Specific Intent**: "Deploy PostgreSQL 15 RDS instance with read replicas for production e-commerce" - **Before**: Same 5 generic questions regardless of specificity - **After**: 3-4 targeted questions about remaining gaps: - Multi-AZ configuration preferences - Backup retention policies - Monitoring dashboard requirements - Open-ended: "Any other considerations..." ## Implementation Plan ### Milestone 1: AI Prompt Enhancement ✨ ✅ **Deliverable**: Unconstrained AI prompt that explores all relevant context - [x] Remove categorical thinking constraints - [x] Enable comprehensive question generation - [x] Add open-ended question requirement ### Milestone 2: Code Constraint Removal 🔓 ✅ **Deliverable**: Recommendation tool that passes through all AI-generated questions - [x] Remove artificial limits and filtering - [x] Support variable question counts - [x] Maintain response quality through AI judgment ### Milestone 3: Documentation Update 📚 ✅ **Deliverable**: Complete user documentation of enhanced clarification process - [x] Document workflow with examples - [x] Update feature descriptions across all files - [x] Show variable question count examples ### Milestone 4: Test Coverage Update 🧪 ✅ **Deliverable**: Comprehensive test coverage for new approach - [x] Variable question count validation - [x] Quality assurance without rigid constraints - [x] Scenario coverage for different intent types ### Milestone 5: Production Validation 🚀 **Deliverable**: Enhanced system validated and ready for users **Progress**: 66% (Implementation complete, broader user validation pending) - [x] All components working together - [x] Documentation accurate and complete - [ ] User experience improved and tested (broader validation needed) ## Risks & Mitigation ### Risk: Too Many Questions Overwhelming Users **Mitigation**: - Users control engagement depth by answering relevant questions - Open-ended question allows quick "N/A" responses - Quality maintained through AI judgment, not artificial limits ### Risk: Question Quality Without Categories **Mitigation**: - AI naturally adapts to intent context - Existing DO/DO NOT guidelines maintain relevance - Real-world testing will validate quality ### Risk: Performance Impact **Mitigation**: - More questions generated but processing remains fast - User experience improved by better first-time matches - Optional `final: true` parameter for skipping clarification ## Dependencies ### Technical Dependencies - ✅ **Claude AI Integration** - Already functional for prompt processing - ✅ **MCP Framework** - Existing tool structure supports enhancement - ✅ **Session Management** - File-based solution storage working ### External Dependencies - **None** - All changes are internal to the dot-ai system ## Success Validation ### Quantitative Measures 1. **Question Count Variance** - Different intents generate different question counts (3-15+) 2. **User Engagement** - Percentage of users who answer clarification questions 3. **Solution Relevance** - Better first-time solution matches ### Qualitative Measures 1. **Question Relevance** - Questions match deployment type and context 2. **Coverage Completeness** - Critical configuration aspects explored 3. **User Satisfaction** - Improved confidence in recommendations ## Implementation Work Log ### 2024-08-30: Complete Implementation (Milestones 1, 2, 3, 4) **Duration**: ~5 hours **Commits**: 3 commits **Primary Focus**: Remove all artificial constraints from intent clarification system **Completed PRD Items**: - [x] AI Prompt Enhancement (Phase 1) - Evidence: `prompts/intent-analysis.md` completely rewritten with comprehensive exploration principles - [x] Code Constraint Removal (Phase 2) - Evidence: `src/tools/recommend.ts` limits removed (`.slice(0, 5)` and impact filtering) - [x] Documentation Updates (Phase 3) - Evidence: Enhanced `docs/mcp-recommendation-guide.md`, `README.md`, and `docs/mcp-tools-overview.md` with clarification workflows - [x] Test Coverage Updates (Phase 4) - Evidence: All 954 tests passing with new JSON structure **Manual Testing Results**: - ✅ Vague intent "Create PostgreSQL database in AWS" → 12 comprehensive questions covering hosting, performance, HA/DR, security, networking, operations, cost, PostgreSQL specifics, timeline + open-ended - ✅ Specific intent → Adaptive targeted questioning (fewer but focused questions) - ✅ No artificial limits constraining question generation - ✅ Domain-specific intelligence (PostgreSQL versions, RDS vs EKS, read replicas) **Architecture Changes**: - Removed rigid categorical thinking (5 fixed categories) - Enabled intent-adaptive question generation - Simplified JSON response structure (removed category/impactLevel fields) - Always include open-ended catch-all question **Progress Update**: All implementation milestones complete (Phase 1, 2, 3, 4) --- ## Future Enhancements ### Pattern Learning - AI learns from successful clarification → solution patterns - Improve question generation based on user interaction history ### Domain Specialization - Enhanced questioning for specific domains (databases, APIs, ML workloads) - Integration with organizational patterns for governance-aware questions ### User Experience Optimization - Smart defaults based on previous answers - Question grouping for better presentation - Interactive clarification workflows