Retrieval-Augmented Generation (RAG)
RAG is the technique of retrieving relevant documents at answer time, then using them to ground an LLM's response. It enables AI search engines to cite sources while generating answers.
Definition
RAG retrieves documents based on query relevance, then feeds them to an LLM alongside the user's question. The model generates its answer using both the retrieved context and its training knowledge. This hybrid approach keeps answers current and allows the system to cite sources, making answers more verifiable than pure generative responses.
Every modern AI search engine, from ChatGPT's web search feature to Perplexity to Google's AI Overviews, uses RAG. The retrieval step decides which sources appear in answers. A source that ranks high in vector search is more likely to be retrieved, and retrieved sources are far more likely to be cited in the final answer.
Why it matters for AI visibility
RAG is the mechanism that determines your brand's AI visibility. Being retrieved in the top sources for relevant queries is the prerequisite for citations. Understanding how RAG systems rank and select sources helps you optimize for AI search engines rather than traditional link-based SEO.
Related terms
Vector Search
Vector Search is the technique of finding semantically similar documents by comparing their embeddings. It powers retrieval in AI search engines and RAG systems.
AIEmbeddings
Embeddings are numerical representations of text, converting words, phrases, or documents into lists of numbers that capture their meaning. AI search engines use embeddings to find relevant sources for answers.
GEOAI Search
AI Search refers to search and discovery systems powered by large language models that generate synthesized answers from multiple sources rather than rank-ordering links in a traditional search results page.
GEOAI Grounding
AI Grounding is the process of constraining LLM generation to reference and cite specific source documents, reducing hallucination and ensuring generated responses are anchored to retrievable content.