RAG

RAG stands for retrieval-augmented generation. It is a technique that searches external sources and incorporates that information into a model’s context to prepare a response. It allows you to work with specific documentation without confusing it with the knowledge learned during training.

How does RAG work?

The system organizes the documents, retrieves relevant fragments, and delivers them to the model along with the question. Quality depends on finding adequate evidence and generating a response that is faithful to it.

How is it applied in marketing?

A support assistant can retrieve a store’s current return policy. The response must reflect its conditions and show the source, without turning an exception into a general rule.

Frequently asked questions about RAG

Is RAG the same as searching on Google?

No. It can retrieve information from an enterprise repository or other configured sources. Web search is one option.

Do you need to update the documents?

Yes. Define owners, versions, and effective dates to prevent the system from retrieving outdated policies.

Does RAG guarantee correct answers?

No. Evaluate retrieval, fidelity to the source, and the ability to recognize when it did not find enough information.

Can it respect internal permissions?

The system must apply access controls when retrieving information. The architecture alone does not guarantee them.

How is its quality measured?

Use questions with verified answers. Check retrieved sources, accuracy, and cases where the assistant should abstain.

RAG: limits you should know

RAG does not automatically eliminate errors. An incomplete search, an old source, or a fragment taken out of context can produce incorrect answers. Check access permissions and behavior when information is missing.

To broaden the context, compare this concept with Embeddings, Grounding and LLM.

Reference source: cloud.google.com. AMD Digital Agency Glossary.