name: "AI-Powered Application"
id: ai-powered-app
description: "LLM-based application with AI capabilities, RAG, function calling, and intelligent features"
estimated_duration: "3-5 weeks"
icon: "microchip"

# Default technology recommendations
defaults:
  ai_provider: anthropic
  framework: nextjs
  vector_db: pinecone
  embedding: openai_embedding
  orchestration: langchain
  streaming: true
  cache: redis

# Alternative technology stacks
alternatives:
  ai_provider:
    - id: anthropic
      name: "Anthropic Claude"
      description: "Best for complex reasoning and safety"
    - id: openai
      name: "OpenAI GPT"
      description: "Wide adoption, strong ecosystem"
    - id: google
      name: "Google Gemini"
      description: "Multimodal capabilities"
    - id: multi_provider
      name: "Multi-Provider"
      description: "Use multiple providers with fallbacks"

  vector_db:
    - id: pinecone
      name: "Pinecone"
      description: "Managed, scalable vector database"
    - id: weaviate
      name: "Weaviate"
      description: "Open-source with hybrid search"
    - id: qdrant
      name: "Qdrant"
      description: "High-performance, self-hosted option"
    - id: supabase_pgvector
      name: "Supabase pgvector"
      description: "PostgreSQL-based, integrated with Supabase"

  orchestration:
    - id: langchain
      name: "LangChain"
      description: "Most popular, extensive tooling"
    - id: llamaindex
      name: "LlamaIndex"
      description: "Best for RAG applications"
    - id: custom
      name: "Custom Implementation"
      description: "Direct API calls, full control"

# Phases of development
phases:
  - id: discovery
    name: "AI Use Case Discovery"
    description: "Define AI capabilities, model selection, and data requirements"
    order: 1
    checkpoint: true
    checkpoint_message: |
      AI discovery complete. Please review:
      - AI use case and expected outcomes
      - Model selection and cost estimates
      - Data requirements and sources
      - RAG requirements (if applicable)

      Approve to proceed with architecture.

    steps:
      - id: use_case
        name: "Use Case Definition"
        agent: planner
        description: "Define specific AI capabilities needed"

      - id: model_selection
        name: "Model Selection"
        agent: architect
        description: "Select AI models based on requirements"

      - id: cost_estimation
        name: "Cost Estimation"
        agent: planner
        description: "Estimate token usage and API costs"

      - id: data_requirements
        name: "Data Requirements"
        agent: planner
        description: "Identify data sources for RAG/fine-tuning"

    outputs:
      - ".omgkit/generated/ai-use-case.md"
      - ".omgkit/generated/model-selection.md"
      - ".omgkit/generated/cost-estimate.md"

  - id: planning
    name: "AI Architecture Planning"
    description: "Design prompt engineering, RAG architecture, and system design"
    order: 2
    checkpoint: true
    checkpoint_message: |
      AI architecture complete. Review:
      - Prompt templates and strategies
      - RAG pipeline design
      - System architecture

      Approve to begin implementation.

    steps:
      - id: prompt_engineering
        name: "Prompt Engineering"
        agent: planner
        description: "Design prompt templates and strategies"

      - id: rag_architecture
        name: "RAG Architecture"
        agent: architect
        description: "Design retrieval-augmented generation pipeline"

      - id: system_design
        name: "System Design"
        agent: architect
        description: "Design overall system architecture"

      - id: evaluation_plan
        name: "Evaluation Plan"
        agent: tester
        description: "Define evaluation metrics and test cases"

    outputs:
      - ".omgkit/generated/prompt-templates.md"
      - ".omgkit/generated/rag-architecture.md"
      - ".omgkit/generated/ai-evaluation-plan.md"

  - id: foundation
    name: "AI Foundation"
    description: "Set up AI SDK integration and vector database"
    order: 3
    checkpoint: false

    steps:
      - id: ai_sdk_setup
        name: "AI SDK Integration"
        agent: fullstack-developer
        description: "Integrate AI provider SDKs"

      - id: vector_db_setup
        name: "Vector Database Setup"
        agent: database-admin
        description: "Set up and configure vector database"

      - id: embedding_pipeline
        name: "Embedding Pipeline"
        agent: fullstack-developer
        description: "Create document embedding pipeline"

      - id: streaming_setup
        name: "Streaming Setup"
        agent: fullstack-developer
        description: "Implement response streaming"

    outputs:
      - "src/lib/ai/"
      - "src/lib/embeddings/"
      - "src/lib/vector-store/"

  - id: core_features
    name: "Core AI Features"
    description: "Implement chat interfaces, completions, and basic AI features"
    order: 4
    checkpoint: true
    checkpoint_message: |
      Core AI features implemented. Review:
      - Chat/completion interfaces
      - Response quality
      - Error handling

      Approve to add advanced features.

    steps:
      - id: chat_interface
        name: "Chat Interface"
        agent: fullstack-developer
        description: "Build conversational AI interface"

      - id: completion_api
        name: "Completion API"
        agent: fullstack-developer
        description: "Create completion endpoints"

      - id: context_management
        name: "Context Management"
        agent: fullstack-developer
        description: "Implement conversation context handling"

      - id: error_handling
        name: "Error Handling"
        agent: fullstack-developer
        description: "Handle API errors, rate limits, fallbacks"

    outputs:
      - "src/app/api/chat/"
      - "src/components/chat/"

  - id: advanced_features
    name: "Advanced AI Features"
    description: "Implement RAG, function calling, and agents"
    order: 5
    checkpoint: true
    checkpoint_message: |
      Advanced AI features complete. Review:
      - RAG pipeline quality
      - Function calling accuracy
      - Agent behavior

      Approve to proceed with optimization.

    steps:
      - id: rag_implementation
        name: "RAG Implementation"
        agent: fullstack-developer
        description: "Implement retrieval-augmented generation"

      - id: function_calling
        name: "Function Calling"
        agent: fullstack-developer
        description: "Implement tool use and function calling"

      - id: agents
        name: "AI Agents"
        agent: fullstack-developer
        description: "Build autonomous AI agents if needed"

      - id: memory
        name: "Long-term Memory"
        agent: fullstack-developer
        description: "Implement conversation memory"

    outputs:
      - "src/lib/rag/"
      - "src/lib/tools/"
      - "src/lib/agents/"

  - id: optimization
    name: "Optimization"
    description: "Optimize for cost, latency, and reliability"
    order: 6
    checkpoint: false

    steps:
      - id: caching
        name: "Response Caching"
        agent: fullstack-developer
        description: "Implement semantic caching"

      - id: rate_limiting
        name: "Rate Limiting"
        agent: fullstack-developer
        description: "Implement rate limiting and quotas"

      - id: cost_optimization
        name: "Cost Optimization"
        agent: fullstack-developer
        description: "Optimize token usage and API calls"

      - id: latency
        name: "Latency Optimization"
        agent: fullstack-developer
        description: "Reduce response latency"

    outputs:
      - "src/lib/cache/"
      - "src/middleware/rate-limit.ts"

  - id: safety
    name: "Safety & Guardrails"
    description: "Implement content moderation and safety measures"
    order: 7
    checkpoint: true
    checkpoint_message: |
      Safety measures implemented. Review:
      - Content moderation
      - Prompt injection defense
      - Output validation

      Approve for deployment.

    steps:
      - id: guardrails
        name: "Guardrails"
        agent: security-auditor
        description: "Implement input/output guardrails"

      - id: moderation
        name: "Content Moderation"
        agent: security-auditor
        description: "Set up content moderation"

      - id: injection_defense
        name: "Injection Defense"
        agent: security-auditor
        description: "Protect against prompt injection"

      - id: eval_suite
        name: "Evaluation Suite"
        agent: tester
        description: "Build evaluation test suite"

    outputs:
      - "src/lib/guardrails/"
      - "src/lib/moderation/"
      - "evals/"

  - id: deployment
    name: "Deployment & Monitoring"
    description: "Deploy with monitoring and A/B testing"
    order: 8
    checkpoint: true
    checkpoint_message: |
      Ready for production. Review:
      - Monitoring setup
      - Cost tracking
      - A/B testing configuration

      This is the final checkpoint.

    steps:
      - id: monitoring
        name: "AI Monitoring"
        agent: cicd-manager
        description: "Set up AI-specific monitoring"

      - id: cost_tracking
        name: "Cost Tracking"
        agent: fullstack-developer
        description: "Implement cost tracking dashboard"

      - id: ab_testing
        name: "A/B Testing"
        agent: fullstack-developer
        description: "Set up prompt A/B testing"

      - id: deploy
        name: "Production Deploy"
        agent: cicd-manager
        description: "Deploy to production"

    outputs:
      - "src/lib/analytics/"
      - ".env.production"

# Autonomy rules for this archetype
autonomy_rules:
  - pattern: "**/prompts/**"
    level: 3
    reason: "Prompts significantly affect output quality"
  - pattern: "**/guardrails/**"
    level: 3
    reason: "Safety measures need careful review"
  - pattern: "**/tools/**"
    level: 3
    reason: "Function calling needs validation"
  - pattern: "**/.env*"
    level: 4
    reason: "API keys are sensitive"
  - pattern: "**/chat/**"
    level: 2
    reason: "Chat UI can be reviewed quickly"
  - pattern: "**/embeddings/**"
    level: 2
    reason: "Embedding pipelines need review"

# Quality gates
quality_gates:
  after_feature:
    - "npm test"
    - "npm run lint"
    - "evals pass"
  before_checkpoint:
    - "npm run build"
    - "eval accuracy >= 80%"
    - "guardrails tests pass"
  before_deploy:
    - "security scan"
    - "cost estimation review"
    - "load testing"

# AI-specific discovery questions
discovery_additions:
  - category: "AI Capabilities"
    questions:
      - "What AI capabilities do you need? (chat, completion, classification, etc.)"
      - "Do you need RAG (Retrieval-Augmented Generation)?"
      - "Will you use function calling/tool use?"
      - "Do you need multi-modal capabilities (vision, audio)?"
      - "Should the AI maintain conversation memory?"

  - category: "Data & Knowledge"
    questions:
      - "What data sources will the AI use?"
      - "How much data needs to be indexed? (documents, size)"
      - "How often is the data updated?"
      - "Are there privacy/compliance requirements for data?"

  - category: "Performance & Cost"
    questions:
      - "Expected request volume? (per day/month)"
      - "Acceptable response latency?"
      - "Monthly budget for AI API costs?"
      - "Do you need fallback models?"

  - category: "Safety & Compliance"
    questions:
      - "Content moderation requirements?"
      - "Do you need to log/audit AI interactions?"
      - "Industry compliance requirements? (HIPAA, SOC2, etc.)"