Phase Deliverables by Track
The GISE dual-track methodology produces different deliverables depending on which track you're following. This guide outlines exactly what you'll create in each phase for each track.
Track Overview
🔍 Discover Phase Deliverables
🔧 LLM-for-Dev Track Deliverables
Primary Deliverables
prompts/discovery/Directory: Collection of reusable prompts for requirements gathering- AI Tool Evaluation Matrix: Assessment of LLM tools for development acceleration
- Development Acceleration Opportunities Map: Identified bottlenecks and AI solutions
Detailed Breakdown
Prompt Library (prompts/discovery/)
prompts/discovery/
├── requirements-clarification.md
├── stakeholder-interview-prep.md
├── user-story-generation.md
├── technical-feasibility-analysis.md
└── risk-assessment.md
AI Tool Evaluation Matrix
| Tool Category | Primary Tools | Use Cases | Integration Complexity | Cost Impact |
|---|---|---|---|---|
| Code Generation | GitHub Copilot, Cursor, Codeium | Boilerplate, API endpoints, test cases | Low | Medium |
| Documentation | Notion AI, GitBook AI | Requirements docs, API specs | Low | Low |
| Analysis | ChatGPT-4, Claude 3.5 | Architecture review, feasibility | Medium | Medium |
| Communication | Slack AI, Teams Copilot | Meeting summaries, status updates | Low | Low |
Development Acceleration Map
- High-Impact Opportunities: Code generation, documentation automation
- Medium-Impact Opportunities: Code review assistance, testing automation
- Low-Impact Opportunities: Meeting summaries, email drafting
Success Metrics
- Time reduction in requirements documentation: Target 40-60%
- Stakeholder interview preparation time: Target 50% reduction
- Requirements clarity score: Target >85% stakeholder approval
🎯 LLM-in-Product Track Deliverables
Primary Deliverables
- User Intent Classification Matrix: Understanding user behavior and routing needs
- RAG Feasibility Study: Assessment of knowledge-base integration opportunities
- AI Feature Value Propositions: Business case for each proposed AI feature
Detailed Breakdown
User Intent Classification Matrix
RAG Feasibility Assessment
- Knowledge Sources: Documentation, FAQs, product specs, user guides
- Update Frequency: Real-time, daily, weekly, static
- Query Complexity: Simple lookup, complex reasoning, multi-step processes
- Integration Points: Existing search, chatbots, help systems
AI Feature Value Propositions
| Feature | User Benefit | Business Value | Technical Complexity | ROI Timeline |
|---|---|---|---|---|
| Smart Search | Faster information discovery | Reduced support tickets | Medium | 3-6 months |
| Chatbot Assistant | 24/7 support availability | Support cost reduction | High | 6-12 months |
| Content Recommendations | Personalized experience | Increased engagement | Medium | 4-8 months |
| Automated Summaries | Quick information consumption | User retention | Low | 2-4 months |
Success Metrics
- User intent classification accuracy: Target >90%
- RAG system relevance score: Target >85%
- AI feature adoption rate: Target >60% within 6 months
📐 Design Phase Deliverables
🔧 LLM-for-Dev Track Deliverables
Primary Deliverables
prompts/design/Directory: Architecture and design-focused prompts- AI Development Tool Configurations: IDE setups, CI/CD integrations
- Automated Quality Guard-rail Checklist: AI-powered quality gates
Detailed Breakdown
Design Prompt Library (prompts/design/)
prompts/design/
├── architecture-review.md
├── api-specification-generation.md
├── database-schema-design.md
├── security-assessment.md
├── performance-optimization.md
└── design-pattern-selection.md
AI Development Configurations
- IDE Setup: Copilot configurations, custom prompts, snippet libraries
- Code Review Automation: PR templates, review checklists, automated checks
- Documentation Generation: API doc automation, README templates
- Quality Gates Integration: Linting rules, testing prompts, security scans
Success Metrics
- Design review time reduction: Target 30-50%
- API specification accuracy: Target >95% first-pass approval
- Security vulnerability detection: Target >90% coverage
🎯 LLM-in-Product Track Deliverables
Primary Deliverables
- RAG System Architecture Document: Complete technical specification
- AI Feature Technical Specifications: Detailed implementation plans
- Model Performance and Latency Budgets: SLA definitions
Detailed Breakdown
RAG Architecture Specification
Technical Specifications Template
## Feature: [AI Feature Name]
### Functional Requirements
- Input handling and validation
- Processing pipeline steps
- Output format and delivery
- Error handling and fallbacks
### Non-Functional Requirements
- Response time: < 2 seconds (95th percentile)
- Availability: 99.9% uptime
- Accuracy: > 85% user satisfaction
- Scalability: 1000+ concurrent users
### Integration Points
- API endpoints and contracts
- Database schema changes
- Third-party service dependencies
- Monitoring and alerting setup
Performance Budgets
| Feature | Latency Budget | Accuracy Target | Cost per Interaction | Scalability Limit |
|---|---|---|---|---|
| Smart Search | < 500ms | > 85% relevance | < $0.01 | 10k queries/hour |
| Chatbot | < 2s | > 90% intent accuracy | < $0.05 | 1k concurrent users |
| Recommendations | < 1s | > 80% click-through | < $0.02 | 50k users/day |
Success Metrics
- Architecture review approval: First-pass acceptance
- Technical specification completeness: 100% coverage
- Performance budget adherence: Within 10% of targets
⚡ Develop Phase Deliverables
🔧 LLM-for-Dev Track Deliverables
Primary Deliverables
- AI-Enhanced Development Workflow: Implemented vibe coding practices
- Automated Testing Suite: AI-generated test cases and quality checks
- Code Review Automation: PR bots and quality guardrails
Development Workflow Enhancements
- Vibe Coding Setup: AI pair programming configurations
- Template Libraries: Code generators and boilerplate automation
- Quality Automation: Linting, testing, and security scanning
- Documentation Pipeline: Auto-generated docs and change logs
Success Metrics
- Development velocity increase: Target 25-40%
- Code quality scores: Maintain or improve existing standards
- Test coverage increase: Target >80%
🎯 LLM-in-Product Track Deliverables
Primary Deliverables
- LLM Microservice Implementation: Production-ready AI services
- Embedding Pipeline Development: Content processing and vector management
- Model Integration Patterns: Reusable integration architectures
Implementation Components
- API Services: RESTful endpoints for AI features
- Processing Pipelines: Data ingestion and embedding generation
- Model Management: Version control, A/B testing, monitoring
- User Interface: Frontend integration and user experience
Success Metrics
- Feature completeness: 100% of specified functionality
- Performance compliance: Meet all latency and accuracy targets
- Integration success: Seamless user experience
🚀 Deploy Phase Deliverables
🔧 LLM-for-Dev Track Deliverables
Primary Deliverables
- AI IDE Rollout Configuration: Team-wide development environment setup
- Development Metrics Dashboard: Productivity tracking and optimization
- Team Productivity Analytics: Performance measurement and improvement
Rollout Components
- Configuration Management: Standardized AI tool setups
- Training Materials: Team onboarding and best practices
- Metrics Collection: Development velocity and quality tracking
- Continuous Improvement: Feedback loops and optimization
Success Metrics
- Team adoption rate: >90% within 30 days
- Productivity improvement: Measurable velocity increase
- Developer satisfaction: >80% positive feedback
🎯 LLM-in-Product Track Deliverables
Primary Deliverables
- Model Hosting & Monitoring: Production AI infrastructure
- A/B Testing Framework: Feature experimentation and optimization
- Customer AI Experience Analytics: User behavior and satisfaction tracking
Production Components
- Infrastructure: Scalable model serving and monitoring
- Experimentation: A/B testing and feature flagging
- Analytics: User interaction tracking and success metrics
- Optimization: Performance tuning and cost management
Success Metrics
- System uptime: >99.9% availability
- User adoption: Meet feature-specific targets
- Business impact: Measurable ROI within defined timeline
Cross-Track Integration
Shared Deliverables
- Governance Framework: Safety, compliance, and quality standards
- Monitoring & Analytics: Performance tracking across both tracks
- Cost Management: Budget optimization for AI tool and model usage
- Knowledge Base: Shared learnings and best practices
Integration Points
Getting Started
- Choose Your Primary Track: Decide whether to focus on LLM-for-Dev or LLM-in-Product
- Review Phase Requirements: Understand deliverables for your chosen track
- Plan Resource Allocation: Budget time and resources for each deliverable
- Start with Discover Phase: Begin building your first deliverables
- Iterate and Improve: Use feedback to enhance deliverable quality
Next Steps
- Single Track Focus: Start with Discover Phase Overview
- Dual Track Implementation: Plan parallel development of both tracks
- Team Coordination: Align deliverables with team roles and responsibilities
Ready to begin? Choose your track and dive into the 4D Methodology to start building these deliverables systematically.