AI Integration Command

Purpose: AI/ML workflows including LLM integration, vector databases, RAG implementation, and ML pipelines

Overview

The ai-integration command provides comprehensive AI/ML integration workflows. With the explosion of AI capabilities, teams need systematic approaches to integrate LLMs, embeddings, and ML models.

Key Use Cases:

• LLM API integration (OpenAI, Anthropic, etc.)
• Vector database setup (Pinecone, Weaviate, Qdrant)
• RAG (Retrieval-Augmented Generation) implementation
• Embedding generation and search
• Prompt engineering and optimization
• ML pipeline development

Workflow

Step 1: Define Use Case

• Identify AI/ML requirement
• Choose appropriate approach
• Define success criteria

Step 2: Setup Infrastructure

Run specialized agents:

• llm-integrator: Integrate LLM APIs
• vector-db-configurator: Setup vector database
• embedding-generator: Generate embeddings

Step 3: Implement Features

• RAG implementation
• Semantic search
• AI-powered features

Step 4: Optimize

• prompt-optimizer: Improve prompts
• model-evaluator: Test and validate

Step 5: Deploy

• ML pipeline setup
• Monitoring configuration
• Cost optimization

Usage Examples

/ai-integration "add semantic search to documentation"
/ai-integration "implement RAG for customer support"
/ai-integration "integrate GPT-4 for content generation"
/ai-integration "setup vector database for embeddings"

Specialized Agents (7)

1. llm-integrator

Integrates LLM APIs (OpenAI, Anthropic, Cohere, etc.)

2. vector-db-configurator

Sets up vector databases (Pinecone, Weaviate, Qdrant, Chroma)

3. embedding-generator

Generates and manages embeddings

4. rag-implementer

Implements RAG (Retrieval-Augmented Generation)

5. prompt-optimizer

Optimizes prompts for better results

6. ml-pipeline-builder

Builds ML training/inference pipelines

7. model-evaluator

Tests and validates AI/ML performance

Output

ai-integration-output/
├── INTEGRATION_PLAN.md       # Integration design
├── IMPLEMENTATION.md         # What was built
├── PROMPTS.md                # Optimized prompts
├── EVALUATION.md             # Performance metrics
└── DEPLOYMENT_GUIDE.md       # Deployment instructions

Priority: NEW - HIGH (emerging importance)