All guides / AI explained
AI explained · Seneca guides
RAG explained: give AI useful knowledge of your business
Your team already has the information. The problem is finding the right answer across manuals, policies, job records and shared folders. Retrieval-augmented generation—usually called RAG—helps AI answer questions using the business information you provide.
How RAG works
RAG combines a search system with a language model. When someone asks a question, the application retrieves relevant passages from an approved collection and gives those passages to the model alongside the question. The model uses that material to prepare an answer, ideally with links or references back to the underlying documents.
Think of it as giving a capable colleague the right pages before asking them to explain something. You do not have to send the whole filing cabinet on every request. The quality of the source material and the search matters just as much as the model’s ability to write a fluent response.
A practical example: answer a service enquiry
A service adviser needs to explain a product’s maintenance interval and warranty procedure. The assistant retrieves the current product manual and the relevant policy, then prepares a response with references. The adviser can check the source and respond without hunting through several systems or asking a colleague to find the same information again.
The gain is less searching, more consistent answers and faster access to knowledge that already belongs to the business. The same approach supports engineers finding procedures, teams checking internal policies and staff preparing tender responses from approved material. It works best when records have clear ownership and current versions.
RAG is different from training a model
Retrieval supplies information at the time of a request. Fine-tuning changes a model’s learned behaviour using training examples, often to improve a particular style, format or task. Adding a new policy to a retrieval system does not mean retraining the language model; the search index needs to incorporate the updated document.
This makes retrieval a practical starting point for changing business knowledge. Fine-tuning can serve a different purpose where the task calls for it. Neither approach makes every answer correct: useful systems make sources easy to inspect and recognise when the available material does not answer the question.
Keep business knowledge under your control
A private RAG deployment can keep the model, document store and search index within your chosen infrastructure. Permissions must follow the user: an assistant should retrieve only material that person is entitled to see. If a hosted model is used, the passages included in the request travel to that service, so routing rules need to reflect your data policy.
Seneca connects the documents, access controls, models and applications around your operation. We help identify where time is being lost and build a business case around useful outcomes. Start with a department or move straight into a broader programme, with document updates and ongoing support included in the design.
A few useful terms
- Embedding
- A numerical representation used to compare the meaning of text or other information.
- Vector search
- Finding information by similarity between those numerical representations.
- Grounding
- Supplying relevant evidence to support an answer.
- Hallucination
- An answer that sounds plausible but is incorrect or unsupported.