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RAG: answers grounded in documents

Learn how retrieval adds relevant sources and where a document-based assistant can still fail.

XYQEN ATLAS · UPDATED SEPTEMBER 26, 2026

Retrieve before generating

Retrieval-augmented generation combines a retrieval step with a language-model response. A system finds potentially useful passages, then includes them in the model’s context. Retrieval may use keyword matching, vector similarity or a combination. The original RAG research connected a pretrained generator with a retrieval component; today the term also describes a wider family of application patterns.

A practical document pipeline

Start with authorized documents. Extract text while retaining page or section references. Split the text into passages that preserve context. Index those passages, retrieve candidates for a question and select the most useful evidence. Ask the model to cite the source IDs actually supplied. Store which passages were used so a reviewer can trace the answer. A citation is a pointer to inspect, not automatic proof.

Access checks come before retrieval

In a multi-user app, retrieval must only search records the current user may access. Filtering a generated answer after it has seen another account’s documents is too late. Apply ownership checks at every data boundary, including downloads, previews, indexes and cached results. Treat retrieved text as untrusted data rather than as authority to change system instructions.

Know when not to answer

Retrieval can miss a key passage, find a misleading excerpt or return outdated material. Generation can still misread the correct source. Evaluate retrieval coverage separately from answer accuracy. If the source set cannot answer the question, say what is missing. Keep uncertainty and source dates visible instead of replacing them with confident prose.

PUT IT INTO PRACTICE

Try it yourself

Download the Evidence Search Lab from Projects. Search the sample passages, then try a question the passages cannot answer. Inspect the scores and source IDs.

What to look for

The lab ranks word overlap, not semantic understanding. A zero score means no shared query terms, and a high score is not a truth or relevance guarantee. It intentionally does not generate an answer.

Sources & further reading

Independent educational material. Examples are simplified and do not replace checking the documentation for the model or service you use.