RAG explained: how an AI assistant uses your own documents
Retrieval-Augmented Generation (RAG) in plain language: how it works, where it helps, typical pitfalls, and what a 4-week pilot looks like.
Language models know a lot about the world, but nothing about your company. They have never seen your manuals, policies or project reports. Retrieval-Augmented Generation, RAG for short, closes this gap without retraining the model.
How RAG works
- Prepare: documents are split into short passages and converted into numerical vectors (embeddings) that capture their meaning.
- Search: when someone asks a question, the system finds the passages with the most similar meaning, even if different words are used.
- Answer: the language model receives the question together with these passages and writes an answer based on them.
- Cite: the answer shows its sources, so every statement can be checked.
Where RAG helps most
RAG is ideal wherever people repeatedly search for information in large document collections: internal help desks, technical documentation for service teams, compliance and policy questions, onboarding new employees, or summarising reports and logs.
Typical pitfalls
- Data quality: outdated or contradictory documents lead to poor answers. Cleaning up the sources is part of the project.
- Access rights: the assistant must only show content the user is allowed to see.
- Evaluation: without a set of real test questions, quality is just a feeling. A good pilot measures it.
- Expectations: RAG answers from your documents; if the answer is not in them, the assistant should say so.
What a 4-week pilot looks like
- Week 1: choose a use case, collect documents and 30–50 real questions.
- Week 2: build the ingestion pipeline and the search.
- Week 3: connect the language model, set up the interface, first tests with users.
- Week 4: measure quality, improve, and decide on the next step together.
Combined with a private LLM, the whole system can run on your own infrastructure, so your documents never leave the company.