Retrieval & ranking
Chunk Retrieval
Fetching the top few most relevant text passages from a vector index for a query.
Chunk retrieval is the step where the system fetches the handful of passages most relevant to a query from its index, usually by comparing vector embeddings. These retrieved chunks are what the model actually reads before writing its answer, so retrieval is the gate: no retrieval, no citation.
Because only the top few chunks are retrieved, relevance at the passage level is decisive. A page that is broadly on-topic but has no single passage closely matching the query may lose to a page with one sharply relevant chunk. This is why precise, question-matched passages beat general coverage for AI citation.
Improving your odds here means writing passages that map tightly to specific buyer questions, so at least one of your chunks is an obvious match when that question is asked.
Related terms
Chunking
Splitting a document into small passages an AI can retrieve and quote one at a time.
Vector Embedding
A list of numbers that encodes meaning so related content sits close in vector space.
Semantic Similarity
How close two pieces of text are in meaning, measured by comparing their embeddings.
Retrieval-Augmented Generation
Fetch relevant documents first, then have an LLM write an answer grounded in them.
Selection Rate
How often an AI cites your page once it's already in the retrieved set.
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