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Glossary · AI

RAG (Retrieval-augmented generation)

Retrieval-augmented generation (RAG) is a technique where an AI system first searches your own documents or data for relevant passages, then gives those passages to a language model so its answer is grounded in your information.

In one line

Retrieval-augmented generation (RAG) is a technique where an AI system first searches your own documents or data for relevant passages, then gives those passages to a language model so its answer is grounded in your information.

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Why RAG matters

A language model only knows what it was trained on. It does not know your policies, product docs or customer records. RAG gives it that context at the moment of the question, without retraining the model.

It also makes answers easier to check, because the system can show which sources it used.

How RAG works

Documents are split into chunks and turned into embeddings, which are stored in a vector database. When someone asks a question, the system finds the most relevant chunks and adds them to the prompt.

The model then writes an answer using those chunks, ideally citing them.

Common RAG mistakes

Poor chunking that splits answers in half. Ignoring access control, so users see documents they should not. Never testing retrieval quality on its own.

Many RAG failures are search failures. If the right passage is not retrieved, the model cannot use it.

How Gr8r approaches RAG

We test retrieval and generation separately, using a set of real questions with known answers. We enforce permissions at retrieval time, show sources in the interface, and design a clear state for when no good source is found.

Example

An internal support assistant searches the help centre and policy documents, then drafts a reply that links to the two passages it relied on, so the agent can check them before sending.

FAQ

Questions

  • Is RAG better than fine-tuning?

    They solve different problems. RAG adds knowledge that changes often. Fine-tuning shapes style or behaviour. Many products use RAG first because it is easier to update and audit.

  • Does RAG stop hallucinations?

    It reduces them but does not remove them. The model can still misread a source or answer when retrieval finds nothing useful, which is why evaluation and source display matter.

  • Can RAG respect user permissions?

    Yes, if permissions are enforced when documents are retrieved. Putting access rules only in the prompt is not enough.

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