Does RAG stop hallucination completely?

No. It removes the most common cause — answering from memory about things it was never told — and makes the rest verifiable by attaching a source. Anything that generates language can still err, which is why the citation matters as much as the retrieval.

Is RAG the same as search?

It contains search but does not stop there. Search returns documents for you to read; RAG uses those documents to compose a direct answer, then shows you where it came from.

Glossary

Retrieval-augmented generation (RAG)

Retrieval-augmented generation means looking up the answer before writing it, rather than recalling it from training.

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Why it matters

A language model on its own answers from statistical patterns learned in training. It has never read your refund policy, and if asked about it will produce something plausible. RAG is the architecture that fixes this by fetching your actual content first.

Models cannot know anything private, recent, or specific to you
Retraining a model on your content is slow, costly, and stale on arrival
Answers without a source cannot be verified by the person reading them

How it works

Your content is split into passages and indexed. When a question arrives, the system searches that index for the passages most likely to contain the answer, puts them into the model's context, and asks it to answer using only that material. The passages it used become the citation.

Retrieve

Find passages semantically related to the question.

Augment

Place them in the model's context as the material to use.

Generate

Produce an answer constrained to that material.

Why it beats fine-tuning for this job

Fine-tuning adjusts a model's behaviour, not its facts, and any facts it does absorb are frozen at training time. RAG updates the moment your content does — publish a page, re-index, and the answer changes. It also produces a citation, which fine-tuning cannot.

What it does not solve

RAG makes the right material available; it does not guarantee the model uses it well, and it cannot retrieve what was never indexed. Retrieval quality — chunking, search, ranking — is where most real-world accuracy problems actually live.

Common questions

Does RAG stop hallucination completely?: No. It removes the most common cause — answering from memory about things it was never told — and makes the rest verifiable by attaching a source. Anything that generates language can still err, which is why the citation matters as much as the retrieval.
Is RAG the same as search?: It contains search but does not stop there. Search returns documents for you to read; RAG uses those documents to compose a direct answer, then shows you where it came from.