Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique where an AI fetches verified passages from a knowledge base before answering, ensuring grounded, factual replies.
Retrieval-Augmented Generation (RAG) is an AI architecture that first retrieves relevant text passages from a private knowledge source, then feeds those passages into a language model to generate an answer grounded strictly in verified facts.
How RAG works in practice
Standard language models answer purely from patterns learned during initial training, making them prone to reciting outdated pricing, deprecated documentation, or entirely fabricated facts. RAG solves this by providing fresh, authoritative context at the exact moment of answering.
When a user submits a question, the RAG engine converts the inquiry into a mathematical query, searches an indexed vector database for the most relevant content chunks, and assembles them into an augmented prompt for the LLM.
Because every claim is linked to specific retrieved passages, the resulting response can include exact source citations, enabling users to verify answers in one click.
How SiteMind implements RAG
SiteMind is a native RAG platform. When visitors chat with your widget, SiteMind performs hybrid search across your crawled website content and generates sub-second, citation-backed answers with Gemini Flash-Lite.
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Related Technical Concepts
Vector Embeddings
Vector embeddings convert text into high-dimensional numerical lists that capture semantic meaning, enabling computers to compare concepts rather than keywords.
Source Grounding & Citations
Source grounding ensures every AI claim is directly supported by retrieved website text and includes clickable citations for instant human verification.
Hybrid Search (Dense + Sparse Retrieval)
Hybrid Search merges dense vector search with sparse keyword matching to deliver superior accuracy across both conceptual and exact-term queries.
Cosine Similarity Thresholds
Cosine similarity measures the angle between two embedding vectors in multidimensional space to determine how closely their meanings match.
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