AI Glossary

The process of splitting documents into smaller, semantically meaningful segments optimised for embedding and retrieval in AI systems like RAG pipelines.

Dense numerical representations of text (or other data) in a high-dimensional vector space, where similar meanings are placed closer together.

A retrieval approach that combines traditional keyword matching (BM25) with semantic vector search to capture both the precision of exact term matches and the contextual understanding of meaning-based search.

A structured representation of information as a network of entities and their relationships, enabling machines to reason about connections between concepts.

A second-stage retrieval technique that re-scores and reorders an initial set of retrieved documents using a more computationally expensive cross-encoder model to surface the most relevant results.

A technique that grounds a language model's output in external data by retrieving relevant documents before generating a response.

A search technique that understands the meaning and intent behind queries rather than matching exact keywords, using vector embeddings to find conceptually relevant results even when different words are used.

Topic clustering is the grouping of related prompts, AI answers, or content pieces into thematic clusters — used in AEO to consolidate prompt-level visibility data into actionable narratives about how AI describes a category.

A database optimized for storing, indexing, and querying high-dimensional vector embeddings, enabling fast similarity search at scale.