Glossary · 4 · Grounding: content, retrieval and knowledge
Retrieval-augmented generation (RAG)
Also known as: RAG
Retrieval-augmented generation (RAG) is an architecture in which an AI system first retrieves relevant passages from a trusted content source and then has a language model generate its answer from those passages, often with citations — so answers reflect current, approved content rather than only the model’s training data.
- Intermediate
- Technical writers
- Technical marketers
- Technical project managers
- Developers
In one sentence
Retrieval-augmented generation (RAG) explained: grounding AI answers in your approved content — why it depends on well-structured documentation.
Example
A customer asks a product chatbot how to reset a controller; the system retrieves the reset procedure for the customer’s firmware version and the model summarizes it with a link to the topic.
Why it matters on your learning path
- Technical writers: RAG makes documentation the knowledge base of AI. Topic-based, metadata-rich, up-to-date content directly improves answer quality.
- Technical marketers: RAG-based assistants can quote product documentation to prospects — accuracy of public content becomes a sales factor.
- Technical project managers: A RAG project is largely a content project: scope, ownership, update process and evaluation of the source content.
- Developers: Key design choices: chunking, embeddings, hybrid keyword and vector search, reranking, metadata filters and citation handling.
RAG vs. fine-tuning
RAG supplies knowledge at the moment of the question and can be updated by updating content. Fine-tuning changes the model’s behavior or style through training. For product knowledge that changes, RAG is almost always the first choice.