What is RAG (retrieval-augmented generation)?
RAG, or retrieval-augmented generation, is a pattern where a system first retrieves relevant passages from your own content and then asks a language model to answer using only those passages. The model supplies the language; your documents supply the facts.
Also known as: retrieval augmented generation, what is RAG.
A RAG pipeline has four stages: ingest and split the source material, index it for retrieval, retrieve the best passages for a question, and generate an answer constrained to what was retrieved. Each stage can be the weak link, and in practice retrieval quality - not model choice - decides whether answers are trustworthy.
RAG is preferred over fine-tuning for knowledge that changes, because you update a document instead of retraining a model, and because every claim can be traced back to a source.
How Uthereal handles it
Cortex is a managed RAG stack: ingestion, chunking, hybrid retrieval, reranking and cited generation, available through an API and the Uthereal SDK.
Auf Deutsch: RAG (Retrieval-Augmented Generation)
RAG bezeichnet ein Verfahren, bei dem zuerst passende Passagen aus den eigenen Inhalten gesucht und anschliessend von einem Sprachmodell zu einer Antwort verarbeitet werden. Die Fakten stammen aus den Dokumenten, nicht aus dem Modell.
Related reading
Last updated 2026-09-18 · plain-text glossary for AI tools