rag

Retrieval-augmented generation grounds LLM responses in your document corpus by retrieving relevant chunks and generating answers cited to specific sources, reducing hallucinations and keeping answers factual and up-to-date.

Ingest your documents into a vector store, embed them for semantic search, retrieve relevant chunks for each query, and have the LLM generate answers that cite the originating document sections, creating a traceable knowledge base.

Yes. Vector search retrieves semantically similar chunks across your document corpus, and multi-source aggregation combines retrieved results so the LLM generates answers that reference and attribute information to specific documents.

You need a vector store for storage and retrieval, an embedding model to vectorize documents and queries, an LLM for answer generation, and your documents prepared in a format the ingestion pipeline can process.

RAG retrieves relevant document chunks before generation, anchoring answers to your corpus with citations. This prevents hallucinations and keeps responses current, unlike LLMs relying solely on training data without source verification.

Retrieval quality depends on embedding model and vector store performance; large document sets require efficient indexing; answer quality is limited by retrieved chunk relevance and LLM reasoning over multi-source contexts.