PIPELINEGlossary · AI and automation
What is RAG?
RAG means that a language model looks up information in a specified document collection before answering and responds only based on what it finds. This ensures the answer comes from verified internal content rather than general training knowledge — and the source can be provided along with the answer.
01 — Context
RAG (Retrieval Augmented Generation) in detail
The process has two steps. First, the question is used to find the relevant passages in your own knowledge base. Then the language model receives exactly those passages along with the instruction to answer exclusively from them.
This solves the core problem of unrestricted language models: they invent plausible-sounding answers when they don't know something. With RAG, the correct answer to an unknown question is not a fabricated one, but an honest “I can't find anything on that” with a referral to a human.
02 — Practice
What this means for small businesses
For businesses, RAG is the reason a website chatbot can be reliable at all. Quality then no longer depends on the model, but on the knowledge base — that is, on how complete and current your own documentation is.
03 — Questions
Frequently asked questions
Does RAG need a particularly large amount of data?
No, well-structured rather than numerous. A maintained FAQ, current service descriptions, and a few sample documents are usually enough to start with.
Do our documents become part of the model with this?
No. They remain in a separate storage and are only supplied at runtime. No training with your data takes place.
04 — Continue
Related terms and services
- Language model (LLM)
- Knowledge base
- Hallucination
- AI phone assistant
- Prompt
- Web accessibility
- Conversion
- Content Management System (CMS)
- CRM system
- All terms
Related services
Next step
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