RAG (Retrieval-Augmented Generation) is an AI application architecture that, before answering, searches a document base for relevant passages and passes them to the LLM so it can rely on them. The term comes from a research paper published by Meta in 2020.
A typical RAG pipeline has three steps:
- ingestion: documents are split into pieces (chunks), turned into embeddings and stored in a vector database;
- retrieval: for each question, the closest pieces by meaning are found, often combined with keyword search;
- generation: the LLM writes its answer from these excerpts, ideally citing its sources.
It is the standard method for making an AI answer on internal or recent data, and for limiting hallucinations. It is easier to update than fine-tuning: you just add documents.
Why it matters when hiring
Building a demo RAG takes a day; making it reliable takes months. A good AI Engineer talks about document chunking, retrieval quality, evaluation sets and access rights to documents. Be wary of candidates who only name frameworks: ask how they measured and improved answer relevance.