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Methodology Overview

The GISE methodology is built around the 4D framework - a systematic approach that takes you from initial concept to production deployment. Each phase has specific deliverables, tools, and GenAI integration points.

Dual-Track GISE Methodology​

GISE v2 introduces a dual-track approach that separates two distinct applications of GenAI:

Track Definitions​

  • 🔧 LLM-for-Dev Track: AI tools and processes that enhance developer productivity and accelerate the software development lifecycle
  • 🎯 LLM-in-Product Track: AI features that ship inside products to deliver direct value to end users

Both tracks follow the same 4D process but produce different deliverables and use different success metrics. You can focus on one track or implement both simultaneously.

4D Decision Records (4DDR) Integration​

GISE methodology includes systematic decision documentation through 4D Decision Records (4DDRs) that capture the rationale behind choices made in each phase:

4DDR Benefits​

  • 📋 Decision Traceability: Track how user research influences technical architecture
  • 🔄 Evolution Management: Document decision changes as requirements evolve
  • 👥 Team Knowledge: Preserve rationale for current and future team members
  • 🤖 LLM Integration: Structured decision context feeds into Blueprint-Plan-Execute workflows

The Complete 4D Workflow​

GIT-First Integration​

Each phase leverages Git branching for rapid iteration and quality control:

Deliverables by Phase​

Phase 1: Discover 🔍​

Core Deliverable: requirements.md + Process Diagrams

Key Activities:

  • Stakeholder interviews with AI-assisted question generation
  • User story creation using LLM templates
  • Process flow documentation with Mermaid diagrams
  • Technology stack research and evaluation

GenAI Integration Points:

  • Requirements clarification prompts
  • User story templates and validation
  • Process diagram generation assistance
  • Technology recommendation analysis

Phase 2: Design 📐​

Core Deliverable: architecture.md + Design Diagrams

Key Activities:

  • System architecture definition with AI guidance
  • Database schema design and optimization
  • OpenAPI specification creation
  • Security and compliance planning

GenAI Integration Points:

  • Architecture pattern suggestions
  • Database schema optimization
  • API design best practices
  • Security consideration analysis

Phase 3: Develop ⚡​

Core Deliverable: Working Code + Tests + Documentation

Key Activities:

  • "Vibe coding" with AI assistance and human oversight
  • Comprehensive test suite development
  • Code quality validation and compliance checking
  • Continuous integration pipeline setup

GenAI Integration Points:

  • Code generation with quality constraints
  • Test case creation and validation
  • Documentation generation and review
  • Configuration file generation

Phase 4: Deploy 🚀​

Core Deliverable: Production System + Operations Guide

Key Activities:

  • Container-based deployment configuration
  • Production environment setup and validation
  • Monitoring and alerting implementation
  • Operational documentation and runbook creation

GenAI Integration Points:

  • Deployment configuration optimization
  • Monitoring setup assistance
  • Documentation generation for operations
  • Troubleshooting guide creation

Quality Gates and Reviews​

Each phase includes structured quality gates using pull requests:

Quality Criteria by Phase:

PhaseQuality GatesSuccess Metrics
DiscoverRequirements completeness, stakeholder approvalClear acceptance criteria, validated user stories
DesignArchitecture review, technical feasibilityScalable design, security compliance
DevelopCode quality, test coverage, performancePassing CI/CD, documented code
DeployProduction readiness, monitoring validationStable deployment, operational documentation

Integration with GenAI Tools​

Integration Principles​

  1. AI as Accelerator: Use AI to speed up routine tasks, not replace critical thinking
  2. Human Oversight: Every AI-generated artifact requires human review and validation
  3. Quality First: AI assistance must maintain or improve quality standards
  4. Documentation: All AI interactions and decisions are documented for team knowledge

Methodology Flexibility​

Adaptation for Different Contexts​

The 4D methodology adapts to various project contexts:

Startup Projects:

  • Rapid discovery and validation
  • Simplified design documentation
  • MVP-focused development
  • Basic deployment and monitoring

Enterprise Applications:

  • Comprehensive discovery process
  • Detailed architecture documentation
  • Extensive testing and compliance
  • Full production deployment with monitoring

Team Sizes:

  • Solo Developer: All phases completed individually with AI assistance
  • Small Team (2-5): Collaborative review process for each phase
  • Large Team (5+): Specialized roles and parallel phase execution

Iteration and Feedback​

The methodology evolves based on:

  • Team feedback from practical application
  • Success metrics measuring effectiveness
  • Technology changes in AI tools and capabilities
  • Industry best practices and emerging patterns

Getting Started with Implementation​

Phase-by-Phase Adoption​

Week 1-2: Discover

  • Learn requirements gathering techniques
  • Practice with AI-assisted story creation
  • Create your first process flow diagrams

Week 3-4: Design

  • Master architecture documentation
  • Learn Mermaid diagram creation
  • Practice API specification writing

Week 5-8: Develop

  • Implement vibe coding workflow
  • Set up AI-assisted testing
  • Establish code quality processes

Week 9-10: Deploy

  • Learn container deployment
  • Set up monitoring and alerting
  • Create operational documentation

Week 11-12: Integration

  • Complete end-to-end project
  • Refine your personal process
  • Share learnings with team

Ready to dive deeper? Choose your next step:

The methodology overview provides the foundation. The real learning happens when you apply these concepts to actual projects.