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Recipe Database - Phase-Organized Approach

The GISE Recipe Database contains proven patterns organized by the 4D methodology phases, with clear separation between LLM-for-Dev and LLM-in-Product tracks. Each recipe combines Mermaid diagrams, code samples, and prompt templates for complete solutions.

Dual-Track Recipe Organization​

GISE v2 organizes recipes by both methodology phase and track focus:

What are GISE Recipes?​

A recipe is a reusable pattern that includes:

🔍 Discover Phase Recipes​

🔧 LLM-for-Dev Track​

Transform development workflow acceleration through AI-assisted discovery:

User Story Generation with AI

Transform requirements into well-structured user stories using AI assistance

Deliverable: prompts/discovery/ directory

Requirements Clarification Prompts

Structured AI prompts for uncovering hidden requirements and edge cases

Deliverable: AI tool evaluation matrix

Stakeholder Interview AI Assistant

AI-powered question generation and analysis for stakeholder interviews

Deliverable: Development acceleration opportunities map

🎯 LLM-in-Product Track​

Define AI features that deliver direct user value:

User Intent Classification

Design systems to understand and classify user intentions using LLMs

Deliverable: User intent classification matrix

Conversational Use-Case Mapping

Map user conversations to business value and technical requirements

Deliverable: RAG feasibility study

Customer Journey AI Enhancement

Identify opportunities to enhance customer journeys with AI features

Deliverable: AI feature value propositions

📐 Design Phase Recipes​

🔧 LLM-for-Dev Track​

AI-powered architecture and design acceleration:

Architecture Diagram Generation

AI-assisted system architecture documentation and diagramming

Deliverable: prompts/design/ directory

API Specification with AI

Generate comprehensive OpenAPI specifications using AI assistance

Deliverable: AI development tool configurations

Database Schema Design Assistant

AI-powered database schema design and optimization

Deliverable: Automated quality guard-rail checklist

🎯 LLM-in-Product Track​

Design AI features for production deployment:

RAG System Architecture

Complete architecture for production-ready RAG systems

Deliverable: RAG system architecture document

Conversational Interface Design

Design patterns for chatbots and conversational AI interfaces

Deliverable: AI feature technical specifications

AI Feature Data Contracts

Define data contracts and API specifications for AI features

Deliverable: Model performance and latency budgets

⚡ Develop Phase Recipes​

🔧 LLM-for-Dev Track​

AI-enhanced development workflows and productivity:

Vibe Coding Workflows

Structured approach to AI-assisted development with human oversight

Deliverable: AI-enhanced development workflow

AI-Assisted Test Generation

Automated test case creation and validation using AI tools

Deliverable: Automated testing suite

Code Review PR Bots

Automated code review and quality checking with AI assistance

Deliverable: Code review automation

🎯 LLM-in-Product Track​

Implement production AI features:

LLM Microservice Implementation

Production-ready microservices for LLM integration

Deliverable: LLM microservice implementation

Embedding Pipeline Development

Scalable pipelines for content embedding and vector management

Deliverable: Embedding pipeline development

Chain-of-Thought Reasoning

Implement CoT reasoning patterns for complex AI decision making

Deliverable: Model integration patterns

🚀 Deploy Phase Recipes​

🔧 LLM-for-Dev Track​

Production deployment of AI-enhanced development tools:

AI IDE Rollout Configuration

Team-wide deployment of AI development tools and configurations

Deliverable: AI IDE rollout configuration

Development Metrics Dashboard

Track productivity improvements and AI tool effectiveness

Deliverable: Development metrics dashboard

Team Productivity Analytics

Measure and optimize team productivity with AI assistance

Deliverable: Team productivity analytics

🎯 LLM-in-Product Track​

Production AI feature deployment and monitoring:

Model Hosting & Monitoring

Production infrastructure for AI model serving and monitoring

Deliverable: Model hosting & monitoring

A/B Testing AI Features

Framework for testing and optimizing AI feature performance

Deliverable: A/B testing framework

Customer AI Experience Analytics

Track user satisfaction and AI feature effectiveness

Deliverable: Customer AI experience analytics

By Complexity Level​

LevelDescriptionPrerequisitesTime Investment
🟢 BeginnerFundamental patterns, step-by-step guidanceBasic development knowledge30-60 minutes
🟡 IntermediateProven solutions, some customization neededComfortable with technology stack1-3 hours
🔴 AdvancedComplex integrations, architecture decisionsExperience with system design3-8 hours

By Technology Stack​

TypeScript/JavaScriptPythonJava/.NETPostgreSQLDockerNext.jsNestJSReact

Recipe Template Structure​

Every GISE recipe follows this consistent structure:

## Recipe: [Name]

### Purpose
Brief description of what this recipe accomplishes and the business value it provides.

### Context
When to use this recipe, prerequisites, and typical scenarios.

### Diagram
```mermaid
[Visual representation of the pattern]

Implementation​

// Code sample demonstrating the pattern

Prompts​

Analysis Prompt:

[Structured prompt for analysis phase]

Implementation Prompt:

[Structured prompt for coding phase]

Validation​

  • Checklist item 1
  • Checklist item 2
  • Quality gate passed

Variations​

Alternative approaches and customization options.


## Featured Recipes

### 🔥 Most Popular

1. **[User Story Generation with AI](discover-phase#user-story-generation)** - Transform requirements into well-structured user stories
2. **[System Architecture Diagram](design-phase#system-architecture)** - Create comprehensive architecture documentation
3. **[Vibe Coding Workflow](develop-phase#vibe-coding-workflow)** - Structured approach to AI-assisted development
4. **[Container Deployment Setup](deploy-phase#container-deployment)** - Production-ready containerized deployment

### 🆕 Recently Added

1. **API Design with OpenAI Specification** - Generate complete API documentation
2. **AI-Assisted Database Migration** - Schema evolution with safety checks
3. **Production Monitoring Dashboard** - Comprehensive observability setup
4. **Team Collaboration Workflow** - Git-based collaboration with AI tools

### 🎯 Quick Wins

1. **Requirements Checklist Generator** - Never miss important requirements
2. **Code Review Prompt Library** - Structured AI-assisted code reviews
3. **Deployment Health Check** - Automated production validation
4. **Documentation Generator** - AI-powered technical documentation

## Using Recipes Effectively

### 1. Selection Process

```mermaid
flowchart TD
    START[Project Need] --> PHASE{Which Phase?}
    PHASE -->|Discovery| DISC[Browse Discover Recipes]
    PHASE -->|Design| DES[Browse Design Recipes]
    PHASE -->|Development| DEV[Browse Develop Recipes]
    PHASE -->|Deployment| DEP[Browse Deploy Recipes]
    
    DISC --> SELECT[Select Recipe]
    DES --> SELECT
    DEV --> SELECT
    DEP --> SELECT
    
    SELECT --> ADAPT[Adapt to Context]
    ADAPT --> IMPLEMENT[Implement Solution]
    IMPLEMENT --> VALIDATE[Validate Results]
    VALIDATE --> FEEDBACK[Provide Feedback]

2. Customization Guidelines​

  • Adapt to your tech stack: Recipes are technology-agnostic but include specific examples
  • Modify prompts: Adjust AI prompts based on your specific requirements
  • Scale complexity: Start simple and add sophistication as needed
  • Document changes: Keep track of modifications for team knowledge

3. Quality Validation​

Each recipe includes validation checklists to ensure successful implementation:

  • Functional validation: Does the solution work as expected?
  • Quality standards: Does it meet code quality and documentation requirements?
  • Integration testing: Does it work within your existing system?
  • Team acceptance: Can other team members understand and maintain it?

Contributing to the Recipe Database​

Recipe Contribution Process​

  1. Identify a Gap: Find a common pattern that isn't documented
  2. Create Recipe: Follow the standard template structure
  3. Test Implementation: Validate the recipe with real projects
  4. Submit for Review: Community review and refinement process
  5. Publication: Added to the database with attribution

Quality Standards​

  • Proven in Production: Recipes must be tested in real projects
  • Clear Documentation: Complete diagrams, code, and prompts
  • Technology Agnostic: Focus on patterns, not specific tools
  • Maintainable: Solutions should be sustainable long-term

Search and Filter​

Use the search functionality to find recipes by:

  • Keywords: Technology names, pattern names, specific terms
  • Phase: Discover, Design, Develop, Deploy
  • Complexity: Beginner, Intermediate, Advanced
  • Technology: Specific programming languages or frameworks

Advanced Filters​

  • Project Size: Solo, small team, enterprise
  • Industry: Startup, enterprise, government
  • Compliance: GDPR, SOX, healthcare, financial
  • Architecture: Monolith, microservices, serverless

Getting Started​

New to GISE Recipes?​

  1. Browse by Phase: Start with recipes for your current project phase
  2. Try a Beginner Recipe: Get familiar with the structure and approach
  3. Adapt and Implement: Customize the recipe for your specific needs
  4. Share Feedback: Help improve the database with your experience

Ready to Dive Deep?​

Choose your path based on current needs:

The recipe database is continuously growing. Check back regularly for new patterns and contribute your own proven solutions to help the GISE community.