What is RAG and what is it for in a business?

The technique that lets an AI answer using a company’s own documents, not just what it learned during training.

Polaris AI Team · Updated on 2026-09-29

The problem it solves

A language model knows what it learned during training: general knowledge, up to a certain date. It does not know a company’s contracts, invoices, internal procedures, or what was discussed with a client last week. Ask it about those and it either does not know or, worse, makes something up that sounds convincing.

Retraining the model on each company’s documents is expensive, slow, and has to be repeated whenever something changes. RAG solves the problem another way: it does not change what the model knows, but what it has in front of it when it answers.

How it works, step by step

• 1. Prepare the archive

  Documents are split into passages, and each passage is converted into a numerical representation of its meaning (an embedding). These representations are stored in an index that allows searching by meaning, not just by exact words.

• 2. Retrieve

  When someone asks a question, the system converts it into the same kind of representation and searches the index for the passages closest in meaning: the contract paragraph about penalties, the invoice from that specific supplier.

• 3. Generate

  The model receives the question together with the retrieved passages and writes the answer from them. A good system shows which documents each piece of information came from, so it can be checked.

What it is for in a business

• Querying internal documentation

  Procedures, manuals, policies: ask in plain language instead of hunting through folders.

• Searching contracts and case files

  Find the clause, deadline, or amount without opening document after document.

• Answering questions about invoices and transactions

  What was paid to a supplier, what is outstanding, when something is due.

• Handling repeat questions

  Let the team or clients get the answer that is already written down somewhere.

Limits

• It is only as good as what it retrieves

  If the search does not find the right passage, the model answers with what it has, which may be incomplete. The quality of a RAG system depends more on how the archive is prepared and searched than on the model.

• Badly scanned or disorganized documents

  A crooked scanned PDF or an unstructured spreadsheet is hard to turn into useful passages. Part of deploying RAG is cleaning up what comes in.

• Permissions

  If everyone can ask about everything, anyone may end up seeing information that is not theirs to see. RAG in a business has to respect who can see what.

On-premise RAG versus cloud RAG

Cloud RAG sends the documents — or at least the retrieved passages and the question — to a provider’s servers so the model can generate the answer. With on-premise RAG, the index and the model sit on the company’s own servers and none of it leaves the network.

For an archive of contracts, medical records, or client files, that difference decides whether it can be used at all. It is the same logic as on-premise AI in general.

Where Polaris AI stands

Today Polaris already answers plain-language questions about the documents it has classified — "which supplier invoices are unpaid?" — but under the hood those queries are resolved with rules and database queries, not with full RAG over the whole archive.

Semantic search across the entire document archive is in development, and is listed as such in the product status section. We say so because it is the difference between what you can get today and what is coming.

Related

• Internal document search with Polaris AI The solution, in detail.
• What is on-premise AI? Why it matters where the model runs.
• AI and GDPR What processing documents with personal data involves.
• AI for law firms Case files, contracts, and internal precedents.