rag-implementation

# RAG Implementation Workflow ## Overview Specialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation. ## When to Use This Workflow Use this workflow when: - Building RAG-powered applications - Implementing semantic search - Creating knowledge-grounded AI - Setting up document Q&A systems - Optimizing retrieval quality ## Workflow Phases ### Phase 1: Requirements Analysis #### Skills to Invoke - `ai-product` - AI product design - `rag-engineer` - RAG engineering #### Actions 1. Define use case 2. Identify data sources 3. Set accuracy requirements 4. Determine latency targets 5. Plan evaluation metrics #### Copy-Paste Prompts ``` Use @ai-product to define RAG application requirements ``` ### Phase 2: Embedding Selection #### Skills to Invoke - `embedding-strategies` - Embedding selection - `rag-engineer` - RAG patterns #### Actions 1. Evaluate embedding models 2. Test domain relevance 3. Measure embedding quality 4. Consider cost/latency 5. Select model #### Copy-Paste Prompts ``` Use @embedding-strategies to select optimal embedding model ``` ### Phase 3: Vector Database Setup #### Skills to Invoke - `vector-database-engineer` - Vector DB - `similarity-search-patterns` - Similarity search #### Actions 1. Choose vector database 2. Design schema 3. Configure indexes 4. Set up connection 5. Test queries #### Copy-Paste Pr...