What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an AI architecture that connects a language model with your own knowledge base to generate answers solely based on provided sources. Large language models like GPT-4 or Llama 3 only know what was in their training data. If you ask such a model about your internal quality manual or your current revenue forecast, it can only guess. It hallucinates a plausible answer that may be wrong. This is where RAG comes in.
RAG couples a language model with your own knowledge base. The system searches your documents for relevant passages, enriches the query with them, and lets the model answer based only on those findings. The answer includes a source you can check. That is the difference between a well-informed employee and one who just talks off the top of their head.
How does RAG work in three steps?
RAG works in three steps: ingesting sources, vector search, and grounding in the prompt.
- Data ingestion and chunking: The first step is preparing your data. A RAG system splits documents into small, meaningful sections. Experts call this process chunking. Chunking is the process by which a RAG system breaks documents into semantically closed sections (typically paragraphs or chapters) to make them individually indexable and searchable. A chunk can be a paragraph, a chapter, or a table. For most enterprise documents, chunk sizes between a paragraph and a chapter have proven effective because smaller chunks increase precision and larger chunks improve context.
- Vector search: From each chunk, the system creates an embedding vector. An embedding is a long series of numbers that mathematically encodes the meaning of the text, similar to invisible GPS coordinates for text snippets. Similar texts get similar number vectors and therefore lie close together. The system stores these vectors in a vector database. When a user asks a question, that question is also converted into an embedding vector. The database then searches for the vectors most similar to the question. This is vector search.
- Grounding in the prompt: The found text passages land together with the original question in a so-called prompt, which is the concrete task we give to the language model. The language model receives the instruction to answer only based on these sources. Experts call this step grounding. The model can no longer freely associate; it is tied to the provided facts. The answer becomes traceable and verifiable.
Data maintenance as the most important lever
The quality of a RAG system depends on data maintenance and retrieval, not on the model. A large, expensive language model is useless if the sources are poor or the system cannot find the right passages. Our position is clear: data maintenance is the most important lever. You must clean your documents, resolve contradictions, and remove outdated information. A RAG system that accesses contradictory sources will confidently deliver wrong answers.
The second lever is retrieval quality. Vector search finds similar texts, but not always the best ones. A reranking method can re-sort the found results and put the most relevant ones at the top. The size of the chunks also matters. A good retrieval process combines vector search with keyword search to find both semantically related results (meaning is taken into account) and exact terms.
Traceability of the answer is another quality feature. The system should not only deliver the answer but also indicate which document and which passage the information comes from. Only then can a user verify the answer. Without this source citation, the answer remains a black box, even if it looks correct.
Access control and GDPR in the enterprise
A RAG system in an enterprise accesses confidential documents. This means you must map access control to the sources. An employee in accounting should not be able to see personnel files. The RAG system must take over access rights from your user directory, i.e., from the same account management that Outlook or Windows login use (e.g., Microsoft Active Directory).
Personal data in the sources is another issue. If your documents contain names, addresses, or other personal information, you must protect them. One option is to replace personal details like names or addresses with placeholders or codes before storing them; experts call this pseudonymization. Another is filtering the answers through a guardrail. A guardrail is an additional check that scans the model's answer for sensitive content and blocks it if necessary. We consider guardrails indispensable as soon as personal data is involved.
Processing should take place within the EU to ensure GDPR compliance. Many cloud providers offer European data centers. Make sure your data does not leave the processing location.
Evaluation and monitoring
Evaluation and monitoring are the third building block. You need to measure whether your RAG system delivers good answers. This includes metrics such as the relevance of the found sources, the correctness of the answer, and the absence of hallucinations. Concrete industry standards like the RAGAS framework or metrics such as Faithfulness and Answer Relevance help systematically capture quality. Without such a measurement process, reliability remains a claim. Build a test set of typical questions and expected answers. Run the system against this test set regularly and track the results over time.
Our position is that a RAG system should not go into production without guardrails and evaluation. The technology is powerful, but it needs a framework of security and control.
What RAG cannot do
RAG is not a silver bullet, but it is the best way to make AI answers verifiable. RAG cannot:
- generate new facts outside the sources,
- write creative texts beyond the template,
- resolve contradictory sources on its own.
For enterprises that want to use AI for internal knowledge work, customer support, or compliance-relevant processes, RAG is currently (as of 2025) the best approach.
Conclusion: Verifiable AI answers
The technology is evolving quickly. New methods like Agentic RAG or Multi-Hop RAG expand the possibilities. But the basic principles remain: good data, good retrieval, clear control. Those who follow these principles can use AI answers that you can source.