RAG

RAG, or Retrieval-Augmented Generation, describes a pattern where an LLM fetches relevant documents at query time and uses them as context for its answer. When a user asks a question, the system first retrieves matching content — via keyword search, semantic embedding search or both — and pastes that content into the prompt. The model then generates a response grounded in those documents instead of relying purely on its trained-in memory.

Most RAG pipelines split the work into two stages. Indexing happens up front: your content is chunked, embedded and stored in a vector database. At query time, the user's question is embedded the same way, the top-N nearest chunks are pulled out, and they are appended to the prompt along with their source URLs. The big payoffs are reduced hallucination, citable answers and the ability to update knowledge without retraining the model.

RAG is decisive for marketers because it is, quite literally, the mechanism behind GEO. AI Overviews, ChatGPT Search, Perplexity, Claude's web mode, Bing and Copilot are all RAG-shaped systems. That means your site has to exist in a form these retrieval pipelines can index, chunk and trust. Clear page structure, semantic headings, short self-contained paragraphs, precise metadata and signals like llms.txt all directly affect how RAG-friendly your content is.

A common misread is that RAG eliminates hallucination. It does not. Pull the wrong chunk and the model hallucinates more confidently than before, with a fake citation attached. Good RAG depends on retrieval quality, chunk design, output format constraints and visible sources working together. 'We added a vector DB' is not a RAG strategy — it is the first 10 percent of one.

Operationally, RAG also has cost implications, especially in Korea. Korean chunks consume noticeably more tokens than English ones, so the same knowledge base costs more to query and refuses to fit into the same context budget. Chunking, deduplication and metadata-driven reranking become a cost lever, not just a quality lever. From a GEO angle, the cheapest way to expand brand visibility is to make sure global RAG systems can ingest your Korean content cleanly in the first place.