Glossary term 26 of 33
Retrieval-augmented generation (RAG)
Generating an answer from live retrieved documents rather than from training data alone.
What does Retrieval-augmented generation (RAG) mean?
Generating an answer from live retrieved documents rather than from training data alone. The architecture that makes current, well-structured content worth publishing.
Quoted without alteration from the glossary of ECHO: How to Make AI Recommend Your Brand.
Which ECHO pillar does Retrieval-augmented generation (RAG) belong to?
Retrieval-augmented generation (RAG) belongs to Hooks, pillar 3 of the ECHO framework. Does it find your content when the question is asked?
The third ECHO pillar. Structured, extractable, self-contained passages that an AI can lift out and attribute without losing meaning. The strongest hooks are ones only you can create: original data, proprietary research, unique analytical framing.
If it trusts you but never surfaces your content for the question being asked, the problem is Hooks. It is built in part 4: hooks, across chapter 7 and chapter 8.
A passage of content retrieved and used on its own, separated from the page it came from.
An explicit source attribution in a generated answer: the link or reference an answer engine shows for a claim.
Anchoring a generated answer in retrieved source material rather than in the model's training data alone.
The third ECHO pillar. Structured, extractable, self-contained passages that an AI can lift out and attribute without losing meaning.
What a document adds beyond what already exists on a topic.
The decomposition of one user question into many simultaneous sub-queries, each retrieving different content, which the system then synthesises into a single answer.
Nearby in the glossary
The glossary runs alphabetically. Query fan-out comes before Retrieval-augmented generation (RAG) and sameAs after it.