Embeddings are lists of numbers that represent the meaning of text, images or other data, arranged so that similar items sit close together. They let software search, group and compare by meaning instead of exact words.
A knowledge graph is a structured network of entities and the relationships between them, stored so that both software and AI models can query connected facts.
Retrieval-augmented generation (RAG) is a pattern that hands a language model relevant passages from your own documents at question time. Its answers then rest on that material, not only on what it learnt in training.
Semantic search matches results by meaning rather than exact keywords, usually by comparing vector embeddings of the query with embeddings of the content. A support portal can return a refund policy article for the query "get my money back" even when those words never appear in it.
A vector database stores embeddings and quickly finds the ones most similar to a query vector, usually alongside metadata filters. It is the common retrieval layer behind semantic search, recommendations and RAG.
In AI, tokenization is splitting text into small units called tokens, such as words or word pieces, which a language model reads and generates. Token counts set how much text fits in a model's context window and usually drive the cost of each API call.
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