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.
Named entity recognition is the task of finding and labelling specific things in text, such as people, companies, places, dates, amounts or product codes. It turns free text into fields a system can store and search.
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.
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 clear proven process, that delivers
You've seen our ideas - now go deeper.
Discover more insights, guides, and engineering practice.