RAG explained simply: How AI answers with your own documents

RAG (Retrieval Augmented Generation, in German roughly “retrieval-augmented text generation”) is a method in which an AI searches your own documents for relevant passages before answering and bases its response on exactly those passages. This allows it to answer questions about manuals, contracts, policies, or project documents and cite the source, without having to train the language model on your data.

Why a language model alone is not enough

A language model such as ChatGPT only knows what was in its training data. It does not know your internal documents. If you still ask it about your travel expense policy, in the worst case it will invent a plausible-sounding answer, a so-called Hallucination. RAG solves this problem by providing the model with the relevant text passages for each question.

How RAG works in three steps

1. Preparation (one-time and ongoing): Your documents are split into sections and indexed so that they can be searched by meaning, not just exact words. It is also recorded who is allowed to see which document.
2. Search (for each question): When someone asks a question, the system identifies the most relevant sections, only from documents that this person is allowed to access.
3. Answers: The language model receives the question together with the retrieved sections and formulates an answer from them, with references to the source documents.

The key point: the model does not answer from its general knowledge, but from your own documents. If it finds nothing suitable, it should say so instead of guessing.

RAG, fine-tuning or simple search?

For questions about company knowledge, RAG is almost always the better choice: New documents are immediately searchable, and every answer can be checked against the source.

What SMEs use RAG for

• Internal knowledge search: Employees ask about processes, policies, or past projects instead of interrupting colleagues. See Find internal documents in seconds.
• Customer service: An AI chatbotanswers customer questions based on your product information and FAQs.
• Departments with extensive documentation: law firms, care, administration or technical fields, see AI document search for tax advisory firms.
• Onboarding: New employees find answers themselves, with a reference to the original document.

What matters in RAG

• Document quality: Outdated or contradictory documents lead to incorrect answers. It is worth cleaning up beforehand.
• Access rights: The search must inherit the permissions from your systems so that no one can use the AI to see documents they would otherwise not be allowed to open.
• Source reference: Each answer should name the documents used so it can be reviewed.
• Data protection and hosting: Where do the search and language model run? For sensitive data, models that are operated in Germany or on-premises may be suitable, see Host AI models in compliance with the GDPR.
• Honesty about gaps: A good system says “I found nothing on that” instead of making up an answer.

The consulting and guided implementation is eligible for funding: via INQA-Coachingthe federal government covers 80% of consulting costs of up to €1,200 per day for up to 12 days, i.e. up to €11,520 in funding. In Mecklenburg-Western Pomerania and the other new federal states, the BAFA consulting grant with 80% is also available until 31/12/2026 (50% in the old federal states). The consulting is funded, not the software. The funding check shows which program is suitable.

Frequently asked questions

What does RAG mean?

RAG stands for Retrieval Augmented Generation. Before answering, an AI searches a defined collection of documents for relevant passages and formulates its answer on that basis, usually with a source reference.

Is the AI model trained with my data in RAG?

No. With RAG, your documents are only searched and the relevant passages are provided for the respective question. The model itself is not changed. Whether a provider uses inputs for its own training must be clarified contractually.

Can RAG give incorrect answers?

Yes, if the documents are faulty or outdated, or if the search finds unsuitable passages. That is why source references are important, so answers can be checked. Compared with a language model without RAG, the risk of fabricated answers is significantly lower.

What is the difference between RAG and fine-tuning?

With fine-tuning, a model is retrained using your own data; with RAG, documents are searched for each question. RAG is more up to date, cites sources, and is usually better suited for company knowledge; fine-tuning is more suitable for style and technical language.

Next step

Our AI document search uses RAG, adopts your access rights, and runs on servers in Germany. The article describes how setup works Set up AI document search. Definition in the glossary under RAG.