Retrieval-Augmented Generation (RAG)
A technique where an AI model retrieves relevant external documents at query time and uses them to ground its generated answer, enabling current and citable responses.
Retrieval-Augmented Generation (RAG) combines a language model with a live retrieval step. Instead of answering only from trained knowledge, the system first fetches relevant documents — from the web or a database — and conditions its answer on them, often citing the sources used.
Why it matters for AEO
RAG is why answer engines can cite current web pages. If your content is retrievable and clearly relevant to a query, it can be pulled into the context the model uses to generate its answer — making your page a potential citation.
What influences retrieval
- Topical relevance and clarity of your content
- Authority and trust signals of the source
- Freshness and factual specificity
- Structure that makes key passages easy to extract
Practical takeaway
Optimizing for RAG-based engines means making your most important facts easy to find, verify, and lift into an answer.
Related Terms
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Embeddings
Numerical representations of text that capture meaning, allowing AI systems to match a query to relevant content based on semantic similarity rather than exact keywords.
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Grounding
The practice of tying an AI model's response to verifiable external sources, reducing fabrication and allowing the answer to cite where its information came from.
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Hallucination
When an AI model generates information that is false, fabricated, or unsupported by its sources while presenting it as fact.