What RAG is, in one sentence you can use in a meeting
Retrieval-augmented generation makes a language model search your own documents first and write its answer from what it found. Compared with using a chatbot on its own, the difference is twofold: the answer comes from your information, and every answer can carry a citation to the document and passage it came from. That is what makes it safe to put in front of a team or a customer.
Where companies put it into production
- Internal support: staff ask about a procedure and get the exact step from the current manual.
- Customer service: answers on terms, warranties or pricing that come from the official document and cite it.
- Legal and procurement: finding clauses across dozens of contracts without reading them one by one.
- Onboarding: new hires ask what they would not dare to ask twice.
- Sales: technical answers and proposals consistent with product documentation.
What decides whether RAG works or stays a demo
Rarely the model. It is how documents are split and labelled, what happens when two versions of a policy contradict each other, who can see what, and how you measure whether an answer was right. RAG without access control shows an employee the contract they should not see; RAG without evaluation cannot tell you when it started answering badly. We design those four pieces before writing any code, because they cannot be bolted on later.
RAG FAQ
- Are my documents used to train the model?
- No. In RAG, documents are retrieved at answer time; they are not used to train any model, and the architecture can keep data inside your own infrastructure.
- What if the answer is not in the documents?
- A well-configured RAG says so instead of inventing. We test that behaviour explicitly with questions we know your sources cannot answer.
- How long until it is live?
- It depends on the volume and state of your documents more than on the technology. The initial source inventory is what allows an honest timeline.
Before we talk
- Private RAG for internal documents
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