A RAG (Retrieval-Augmented Generation) pipeline is an AI architecture that retrieves relevant documents from a knowledge base and feeds them as context to a large language model to produce accurate, grounded responses.
Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines information retrieval with text generation. Instead of relying solely on an LLM's training data, a RAG pipeline first searches a knowledge base for relevant documents, then passes those documents as context to the LLM for response generation. This approach dramatically reduces hallucinations, enables the AI to answer questions about proprietary or recent data, and provides citations for generated content.
A typical RAG pipeline has three stages: - Indexing — Documents are split into chunks, converted to vector embeddings, and stored in a vector database (e.g., Pinecone, Weaviate, pgvector) - Retrieval — When a user asks a question, the query is embedded and used to find the most semantically similar document chunks - Generation — The retrieved chunks are inserted into the LLM prompt as context, and the model generates a grounded response Advanced RAG techniques include query rewriting, hybrid search (combining keyword + semantic search), re-ranking retrieved results, and multi-hop retrieval for complex questions.
OpenClaw provides several skills for building production RAG pipelines: - RAG Pipeline (`npx clawhub@latest install rag-pipeline`) — End-to-end retrieval-augmented generation with support for multiple vector stores - Deep Research (`npx clawhub@latest install deep-research`) — Multi-source research with RAG-enhanced synthesis - Doc Ingestor — Chunk and embed documents from PDFs, web pages, and databases These skills handle the complexity of chunking strategies, embedding model selection, and prompt engineering so developers can focus on their specific use case.
RAG retrieves external knowledge at query time without modifying the model, while fine-tuning permanently adjusts model weights with new data. RAG is better for frequently updated knowledge bases; fine-tuning is better for teaching new behaviors or styles.
OpenClaw RAG skills support Pinecone, Weaviate, Qdrant, ChromaDB, pgvector (PostgreSQL), and Milvus. You can also use local in-memory stores for development.
Use specific, well-chunked documents; implement re-ranking to surface the most relevant results; set temperature to 0 for factual queries; and instruct the LLM to only answer based on provided context.