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.
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 NoSQL database stores and retrieves data without the fixed tables and relations of traditional SQL systems. It uses documents, key-value pairs, wide columns or graphs, and is often built to scale horizontally.
RAG gives a language model relevant documents at the moment it answers, while fine-tuning retrains the model's weights on custom examples. RAG suits changing facts that need citing; fine-tuning suits fixed styles, formats or specialised skills.
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.
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