Retrieval-Augmented Generation (RAG)

RAG is an AI architecture that combines information retrieval with language generation. Instead of relying solely on what a language model learned during training, RAG first searches a knowledge base for relevant documents, then includes those documents as context when generating a response. The typical RAG pipeline has three stages. First, a user query is used to search a knowledge base — usually a vector database containing embedded documents. Second, the most relevant documents are retrieved and ranked. Third, these documents are included in the prompt alongside the user's question, and the language model generates a response grounded in the retrieved information. RAG addresses two fundamental limitations of language models: their knowledge is frozen at training time, and they cannot access private or proprietary information. By retrieving up-to-date documents at query time, RAG ensures responses reflect current information. By connecting to internal knowledge bases, it enables AI that understands your organisation's specific context.

RAG is the most widely adopted architecture for enterprise AI applications because it solves the core challenge of making AI useful with organisational knowledge. It is faster and cheaper to implement than fine-tuning, can be updated instantly by modifying the knowledge base, and provides traceable sources for every response. Common enterprise RAG applications include internal knowledge assistants (searching company documentation, policies, and procedures), customer support systems (drawing from product manuals and FAQ databases), legal research tools (searching case law and contracts), and compliance assistants (referencing regulatory documents). The quality of a RAG system depends on multiple factors: the quality of document chunking and embedding, the effectiveness of the retrieval strategy, the relevance of re-ranking, and the prompt engineering that combines retrieved context with the query. Each of these components can be optimised independently, making RAG systems highly tuneable.