.svg)
Kronehoefstraat 85
5612 HL Eindhoven
BTW NL863902509B01
What is it?
Retrieval-Augmented Generation (RAG) is a technique in which a language model, before generating a response, retrieves relevant passages from an external knowledge source. Those retrieved passages are included as context in the prompt, so the model bases its answer on your specific documents rather than solely on its training data.
RAG is the most widely used approach for connecting language models to business-specific knowledge: contracts, procedures, client files, regulations. Without RAG, a model answers from general training data that may be outdated or unaware of your context. With RAG, it answers from what you provide.
Why it matters for SMEs
For SMEs, RAG is the difference between a generic AI answer and one that fits your business context. Most practical AI applications in SMEs require access to internal documents: contracts, rates, policies, client history. RAG is the way to make that connection without retraining the model.
- Current and accurate output: the model grounds its answer in documents you supply, not in outdated training data. That is essential for regulations, pricing, or client-specific information that changes regularly.
- Fewer hallucinations: because the model anchors its response in provided text, the chance of fabricated information is substantially lower than with a model given no context.
- No retraining required: you connect new or updated documents to the system without retraining the model. Updates to your knowledge base are immediately available.
RAG is now the standard approach for knowledge-driven AI applications in SMEs: from customer service bots that answer based on the current price list, to internal assistants that help staff with contracts or HR policies.
How it works
RAG works in two phases: a retrieval phase that fetches the relevant information, and a generation phase in which the language model uses that information to compose a response.
- Store documents as embeddings: all relevant sources, such as PDFs, manuals, or contracts, are converted into numerical representations and stored in a vector database such as Pinecone, Weaviate, or Chroma.
- Convert the question: the user's question is also converted into an embedding and compared with the stored documents.
- Retrieve relevant passages: the most similar passages are selected based on semantic similarity, not exact word matching.
- Add context: the retrieved passages are placed together with the question as context in the prompt.
- Generate a response: the language model produces an answer grounded in the supplied context, bound to what those documents contain.
The quality of RAG depends entirely on the quality of the knowledge source. Outdated, inconsistent, or poorly structured documents produce unreliable output, even when the model itself performs well.
Example in practice
Picture an accounting firm that stores the annual accounts, tax returns, and correspondence of a hundred clients in a document repository. With RAG, a staff member can ask questions such as "What was this company's VAT payment in the third quarter?" or "Are there any outstanding commitments in this client's file?" The system retrieves the relevant passages from the correct client dossier and gives an answer grounded in those specific documents. The staff member does not need to search manually through files and receives the answer immediately, tied to the actual content of the records.
Comparison and misconceptions
Fine-tuning adapts the model itself on new data and is costly and time-consuming; RAG connects the model to current documents without retraining it. RAG is the right choice for business-specific knowledge that changes regularly. Fine-tuning adds value when you need a consistently different writing or reasoning style that you cannot achieve through prompts.
.svg){kind=icon}