RAG: How to Ground LLM Responses in Your Own Business Data

Retrieval-Augmented Generation (RAG) is a technique that allows large language models to answer questions using a company's own documents rather than relying solely on pre-trained knowledge. The process involves indexing documents by splitting them into chunks and converting them into vector embeddings stored in a vector database. When a user asks a question, the most relevant document fragments are retrieved and passed to the LLM alongside the query as context. Instructing the model to answer only from the provided context significantly reduces hallucinations and enables source citation. RAG is considered a cost-effective AI deployment strategy since it requires no model retraining — the assistant improves simply by updating the underlying documents.