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Discoverability surfaces
Architecture where a model retrieves relevant documents from an external knowledge store (vector database, search index, or hybrid) before generating an answer. RAG addresses three core LLM limitations: knowledge cutoff dates, hallucination on out-of-distribution facts, and the inability to cite sources. Most enterprise LLM deployments are RAG systems; the retrieval layer typically uses an embedding model plus a vector database like Pinecone, Weaviate, Qdrant, or Milvus.
Programmatic SEO, AEO, GEO, AIO, and the schemas behind them.
A content strategy that generates hundreds or thousands of search-optimized pages from structured data using templates.
The practice of structuring website content so that AI assistants and large language models (LLMs) can accurately cite it when answering user questions.
An open protocol that allows websites to notify search engines (Bing, Yandex, Seznam, Naver, and others) about new or updated content in real time.
Structuring content so that answer engines — Google's People-Also-Ask, Reddit pull-quotes, Quora top answers, ChatGPT search results, Perplexity citations — can extract a complete, self-contained answer in 40–80 words.
The subset of GEO/AEO targeted specifically at Google's AI Overviews (formerly SGE).
A Schema.
JavaScript Object Notation for Linked Data — the W3C-standard syntax for embedding structured data in web pages.
A Schema.
This definition is published under CC BY 4.0. Cite as:
The Data Nerd. "Retrieval-Augmented Generation (RAG)." VC Deal Flow Signal Glossary, https://signals.gitdealflow.com/define/rag.
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