Grounding an LLM's answers in retrieved documents so it responds from real, current data.
Retrieval-augmented generation is a technique that gives a large language model access to specific, up-to-date information at the moment it answers, instead of relying only on what it learned during training. When a question comes in, the system first retrieves the most relevant documents from a knowledge source, then hands them to the model as context so its answer is grounded in that material.
RAG is the standard way to build assistants that answer accurately from your own content, policies, or product data without retraining a model every time the information changes.
Your documents are split into chunks and converted into embeddings stored in a vector database. At query time the system finds the chunks most relevant to the question, injects them into the prompt, and the model composes an answer from that retrieved context, ideally citing it.
RAG dramatically reduces hallucination and lets an assistant stay current: you update the knowledge base, not the model. It also keeps proprietary data out of training and lets answers cite their sources, which builds trust.
We build RAG knowledge assistants over a client's documentation, support content, and product data, so staff and customers get accurate, cited answers grounded in the client's own material rather than a model's guesswork.
An AI model trained on vast text to predict and generate language, powering tools like ChatGPT.
A defined contract that lets two software systems talk to each other in a predictable way.
Optimising content to be cited and surfaced by AI answer engines, not just ranked in blue links.
Common questions
Straight answers on how this fits your marketing and build.
Fine-tuning changes the model's weights and is slow and costly to keep current. RAG leaves the model alone and feeds it fresh information at query time, so you update a knowledge base instead of retraining.
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