Retrieval-augmented generation, usually abbreviated to RAG, is an AI technique that combines a large language model with a retrieval step that searches a specific set of documents or data before generating a response. Rather than relying solely on what the AI model learned during training, RAG retrieves relevant, verified information at the moment a question is asked and uses it to ground the answer.
How retrieval-augmented generation works
When a user asks a question, a RAG system first searches a defined knowledge source, such as a company's internal documents or a product manual, using vector search to find the most relevant passages. These retrieved passages are then provided to the large language model alongside the original question, and the model generates an answer based on that specific, verified content rather than from general training knowledge alone. This significantly reduces the risk of AI hallucination and means answers can be traced back to a source document.
RAG in practice for UK businesses
- A professional services firm builds a RAG-based internal tool that lets staff ask natural language questions about company policies, with answers grounded in the actual policy documents rather than guesswork.
- A technology support team uses a RAG system trained on product documentation, allowing customers to get accurate, source-grounded answers to technical questions through a chatbot.
- A business combines RAG with Azure OpenAI to build a tool that answers questions about its own contracts and compliance documents, with each answer linked back to the specific clause it was drawn from.
- A research team uses a RAG tool to summarise findings across a large set of reports, with citations back to the original source documents for verification.
How Advantage helps businesses build RAG solutions
Advantage advises on where RAG-based solutions can add genuine value, particularly for businesses wanting AI that answers questions grounded in their own documents rather than generic AI responses, and works with technical partners to scope and build these solutions appropriately.
Frequently asked questions
Why is RAG used instead of relying on the AI model alone?
Large language models can only answer based on what they learned during training, which has a fixed cutoff and may not include an organisation's private documents at all. RAG retrieves relevant, up-to-date information at the moment a question is asked, grounding the AI's response in verified source material rather than relying solely on the model's training data.
Does RAG completely eliminate AI hallucination?
No, but it significantly reduces the risk. Because the AI model generates its answer based on retrieved source documents, RAG-based responses are typically more accurate and can be checked against the original source. Hallucination can still occur, particularly if the retrieval step fails to find the most relevant documents.
Is RAG only used for chatbots?
No. While RAG is commonly associated with chatbots that answer questions about internal documents, the same underlying technique is used in other applications, such as customer support tools that draft responses grounded in a knowledge base, or research tools that summarise findings from a specific document set.
