# Mastra RAG RAG (Retrieval-Augmented Generation) is [[Mastra AI]]'s document-processing and semantic-search pipeline that grounds LLM outputs in your own data, improving factual accuracy and reducing hallucination. Mastra exposes it as composable steps rather than a black box. ## Pipeline 1. **Document initialization**: load source documents 2. **Chunking**: split into retrievable units 3. **Embedding**: convert chunks to vectors (see [[Embeddings]]) 4. **Vector storage**: persist to a vector DB (pgvector, Pinecone, Qdrant, MongoDB) 5. **Query retrieval**: embed the query and fetch the most relevant chunks to inject into the prompt ## References - https://mastra.ai/docs/rag/overview ## Related - [[Mastra AI]] - [[Mastra Agents]] - [[Mastra Memory]] - [[Embeddings]] - [[Large Language Models (LLMs)]]