RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.

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 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 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 Prompts Phase 4: Chunking Strategy Skills to Invoke rag engineer Chunking strategies rag implementation RAG implementation Actions 1. Choose chunk size 2. Implement chunking 3. Add overlap handling 4. Create metadata 5. Test retrieval quality Copy Paste Prompts Phase 5: Retrieval Implementation Skills to Invoke similarity search patterns Similarity search hybrid search implementation Hybrid search Actions 1. Implement vector search 2. Add keyword search 3. Configure hybrid search 4. Set up reranking 5. Optimize latency Copy Paste Prompts Phase 6: LLM Integration Skills to Invoke llm application dev ai assistant LLM integration llm application dev prompt optimize Prompt optimization Actions 1. Select LLM provider 2. Design prompt template 3. Implement context injection 4. Add citation handling 5. Test generation quality Copy Paste Prompts Phase 7: Caching Skills to Invoke prompt caching Prompt caching rag engineer RAG optimization Actions 1. Implement response caching 2. Set up embedding cache 3. Configure TTL 4. Add cache invalidation 5. Monitor hit rates Copy Paste Prompts Phase 8: Evaluation Skills to Invoke llm evaluation LLM evaluation evaluation AI evaluation Actions 1. Define evaluation metrics 2. Create test dataset 3. Measure retrieval accuracy 4. Evaluate generation quality 5. Iterate on improvements Copy Paste Prompts RAG Architecture 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 Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

rag-implementation