Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation is a method where an AI system retrieves relevant information from a knowledge base before generating an answer. Instead of relying solely on the language model's training knowledge, RAG actively searches for the most recent and relevant documents. This produces answers that are well-founded, current, and source-based.

RAG works in three steps: First, the user question is analyzed and converted into a search vector. Second, the system searches a vector database containing your company documents and finds the most relevant text passages. Third, the language model receives these passages as context and generates a precise answer directly based on your data.

RAG solves one of the biggest problems with conventional AI: hallucinations and outdated knowledge. For businesses, this means reliable, source-based answers from your own documents, manuals, and knowledge bases. New employees find answers immediately, customers receive precise product information, and your knowledge becomes available 24/7.

A machinery manufacturer with 500 technical manuals implements RAG. Service technicians ask the system questions like 'Error code E-47 on model XR-200' and instantly receive the relevant instructions with page references - instead of searching the archive for 20 minutes.