Retrieval-Augmented Generation (RAG)
An architecture pattern that grounds language model outputs in retrieved documents from a private corpus, reducing hallucination and enabling answers based on the firm's own data rather than the model's training set.
How it works
RAG is how enterprises actually deploy language models on internal knowledge. The pattern is: index the firm's documents into a vector database, retrieve the top-k most relevant chunks for a given user question, and pass them to the language model as context for answer generation. The output is grounded in the firm's own material, with citations back to source documents. RAG works because the language model does not need to know the firm's data; it only needs to read and reason over it at inference time. For UK professional services firms, NHS trusts, FCA-regulated firms, and engineering organisations, RAG on a private corpus is the standard architecture for knowledge retrieval, contract review, clinical-letter triage, and engineering-knowledge access.
Related terms
Vector Database
A database optimised for storing and querying high-dimensional vector embeddings using approximate nearest-neighbour algorithms, used as the retrieval layer in RAG systems and semantic search.
Vector Embedding
A high-dimensional numerical representation of text, image, or other content that places semantically similar items close together in vector space, enabling similarity search and clustering.
Large Language Model (LLM)
A neural network trained on large text corpora to predict the next token given context, used for text generation, summarisation, classification, and reasoning tasks across enterprise software.
Private AI
AI deployed on infrastructure the client controls (on-premise, in the client's cloud tenancy, or air-gapped), with no third-party LLM provider in the data path and no inference-time data export.
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