RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is the core architecture of answer engines: user query → vector-retrieve relevant pages → feed retrieved chunks to an LLM to synthesize the answer. This means content must be crawlable, chunkable, and embeddable.
RAG (Retrieval-Augmented Generation) is the core architecture of answer engines: user query → vector-retrieve relevant pages → feed retrieved chunks to an LLM to synthesize the answer. This means content must be crawlable, chunkable, and embeddable.
What is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) is the core architecture of answer engines: user query → vector-retrieve relevant pages → feed retrieved chunks to an LLM to synthesize the answer. This means content must be crawlable, chunkable, and embeddable.
Details and examples
GEO implication: clear subheadings, self-contained paragraphs, no long nested sentences — so the chunker can produce clean citable blocks.
What are common misconceptions about RAG (Retrieval-Augmented Generation)?
RAG is not a model — it's a model + retrieval architecture A vector DB is not required; keyword retrieval + rerank also counts as RAG
Related terms
Chunking, Vector Search, Answer Engine.
RAG (Retrieval-Augmented Generation) · term at a glance
| Field | Value |
|---|---|
| Term | RAG (Retrieval-Augmented Generation) |
| Category | Technical |
| Related | Chunking, Vector Search, Answer Engine |
Frequently asked questions
What is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) is the core architecture of answer engines: user query → vector-retrieve relevant pages → feed retrieved chunks to an LLM to synthesize the answer. This means content must be crawlable, chunkable, and embeddable.
What is the most common misconception about RAG (Retrieval-Augmented Generation)?
RAG is not a model — it's a model + retrieval architecture
References
By the 23SEOGEO team · Last updated: