🧭 Search, Embeddings and RAG

A citation in an AI answer points you to a source passage or document intended to support a specific claim.

A search method that finds documents by matching query words to indexed text and ranking the matches, often with scoring methods such as BM25.

An organised set of trusted documents that a search system can look through to find relevant passages for an answer.

Retrieval returns passages that a search method ranks as relevant, so a person or later model step can use source material.

A search method that finds material by similar meaning, not just shared words, and returns the most relevant passages for review or further AI use.

A measure of how close two pieces of text are in meaning, even if they use different words.

Chunking splits source material into smaller pieces so search and retrieval can find and return the most relevant passage, rather than an entire document or a random fragment.

Grounding ties an AI answer to specific retrieved source passages so the reply is based on provided material rather than only on the model's general patterns.

Indexing organises documents or document chunks into a searchable structure so a system can quickly find the most relevant passages for a query.

A system orders possible results by estimated fit to a query, then may score and reorder a shortlist with a second method.

A method that first finds relevant source passages and then asks a language model to answer using that material.

A specialised system stores numerical meaning-representations of content and quickly finds the closest matches to a query by semantic similarity.

A measure of how closely two non-zero vectors point in the same direction, commonly used to compare embeddings in search.

A method for finding the items most similar to a query in a vector space; approximate methods often trade a little accuracy for much faster search at scale.