Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation, or RAG, is an architecture for getting a large language model to answer from a specific body of knowledge instead of only from what it absorbed during training. Rather than asking the model to recall an answer, a RAG system first retrieves the passages most relevant to the question from a chosen source — a help center, a product catalog, a set of documents — and passes them to the model as context. The model then composes its reply from that supplied material.

The pattern exists to solve two problems with using a language model on its own. First, a model's training data is fixed and general; it does not know your prices, your policies, or anything published after its cutoff. Second, a model asked about something it does not know tends to invent a plausible-sounding answer: a hallucination. Retrieval addresses both by putting the right, current source text in front of the model at the moment it answers.

The two stages

A RAG system has a retrieval stage and a generation stage, and its quality is capped by the weaker of the two. Retrieval usually combines semantic search (matching on meaning via embeddings) with keyword search, then reranks the candidates so the best passage rises to the top. Generation then turns those passages into a fluent answer. A capable model cannot rescue bad retrieval: if the right passage was never fetched, the model has nothing correct to work from, and a confident wrong answer is the usual result.

Why grounding and citations matter

The value of RAG is not just accuracy but traceability. Because the answer is built from identifiable source passages, a well-built system can show which sources it used, letting a human verify the reply and letting the system decline when nothing relevant was found. That "I don't know" behavior, refusing rather than guessing when retrieval comes up empty, is a feature of good RAG, not a failure of it.

RAG in Evoriqa

Evoriqa is a RAG system built for support. It indexes your own knowledge (a crawled site, uploaded PDFs and DOCX, pasted text, and FAQ pairs) and answers with hybrid retrieval that combines semantic and keyword search and reranks the results, so the best-matching passage wins. Every answer stays traceable to its sources in your dashboard, and the agent says "I don't know" and hands off instead of inventing facts when the knowledge genuinely is not there. You can pressure-test all of this in a sandbox playground before customers see it, watching which sources each answer pulled and how confident it was.