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 Define use case Identify data sources Set accuracy requirements Determine latency targets 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 Evaluate embedding models Test domain relevance Measure embedding quality Consider cost/latency 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 Choose vector database Design schema Configure indexes Set up connection Test queries Copy-Paste Prompts Use @vector-database-engineer to set up vector database Phase 4: Chunking Strategy Skills to Invoke rag-engineer - Chunking strategies rag-implementation - RAG implementation Actions Choose chunk size Implement chunking Add overlap handling Create metadata Test retrieval quality Copy-Paste Prompts Use @rag-engineer to implement chunking strategy Phase 5: Retrieval Implementation Skills to Invoke similarity-search-patterns - Similarity search hybrid-search-implementation - Hybrid search Actions Implement vector search Add keyword search Configure hybrid search Set up reranking Optimize latency Copy-Paste Prompts Use @similarity-search-patterns to implement retrieval Use @hybrid-search-implementation to add hybrid search Phase 6: LLM Integration Skills to Invoke llm-application-dev-ai-assistant - LLM integration llm-application-dev-prompt-optimize - Prompt optimization Actions Select LLM provider Design prompt template Implement context injection Add citation handling Test generation quality Copy-Paste Prompts Use @llm-application-dev-ai-assistant to integrate LLM Phase 7: Caching Skills to Invoke prompt-caching - Prompt caching rag-engineer - RAG optimization Actions Implement response caching Set up embedding cache Configure TTL Add cache invalidation Monitor hit rates Copy-Paste Prompts Use @prompt-caching to implement RAG caching Phase 8: Evaluation Skills to Invoke llm-evaluation - LLM evaluation evaluation - AI evaluation Actions Define evaluation metrics Create test dataset Measure retrieval accuracy Evaluate generation quality Iterate on improvements Copy-Paste Prompts Use @llm-evaluation to evaluate RAG system RAG Architecture User Query -> Embedding -> Vector Search -> Retrieved Docs -> LLM -> Response | | | | Model Vector DB Chunk Store Prompt + Context Quality Gates Embedding model selected Vector DB configured Chunking implemented Retrieval working LLM integrated Evaluation passing Related Workflow Bundles ai-ml - AI/ML development ai-agent-development - AI agents database - Vector databases