RAG: citable answers from your own knowledge
Definition
RAG is a method where a language model searches an own knowledge source for relevant passages before every answer and builds them into the reply. Instead of drawing only on its training, the model reads current, approved documents alongside. This produces answers with origin, and your assistant reaches company knowledge no general model has ever seen.
Why the Term Matters
A language model on its own only knows what its training data held at the time. For your company that is too little, since contracts, handbooks and tickets are neither public nor current. RAG closes that gap: the assistant fetches the right passage from your knowledge and answers with a reference. That is what turns a nice chat window into a reliable company brain.
When It Is Relevant
- Answers must rest on concrete, approved documents.
- Knowledge changes often, so a freshly trained model would age fast.
- Users want to see and check the source.
- Permissions decide who may receive which content as an answer.
When Not
When a question concerns pure general knowledge or no own source exists, RAG adds nothing and only creates extra effort. For very small, stable amounts of text it is often enough to place the content directly in the prompt rather than build a retrieval layer.
Industries That Use It
- Tax advisory: an assistant that answers from client files and statutes with exact reference points.
- Mechanical engineering: a company brain over maintenance manuals and inspection records that shows service the right passage.
- Insurance: answers from tariff documents and terms, cleanly tied to the specific policy.
- Education and publishing: an assistant that draws only from vetted teaching material.
Typical Use Cases
- A support assistant that cites from manuals instead of guessing.
- An internal question-answer system over scattered project documents.
- A research assistant that gathers proposals from earlier tenders.
- An onboarding helper that serves new staff from the wiki.
- A sales assistant that pulls product detail from the price list.
Decision Guide
| Signal | RAG fits | Alternative |
|---|---|---|
| Answer needs an own source | retrieval layer plus model | a bare model guesses |
| Knowledge changes constantly | index the documents | fine-tuning ages fast |
| Origin must be visible | return the passage | answer without proof |
| Only general knowledge asked | rather not | ask the model directly |
Example
A team places its handbooks and tickets into a searchable knowledge base and connects it to the assistant. When someone asks about a process, the system first finds the fitting paragraphs and forms an answer with a reference. Anyone without read rights on a document does not see its content either. This describes the method, not an asserted metric.
Service Connection
RAG is the technical backbone of the company brain and of a personal AI assistant. For your own workflows it embeds into AI agents.
Sources
This framing describes common practice around Retrieval Augmented Generation. Quality, permissions and data protection depend on how the sources are prepared, on retrieval and on the chosen model, and must be checked case by case.
