Loading
Loading
Semantic search retrieves information based on meaning rather than exact keyword matches, usually by comparing embeddings in vector space.
It helps users find relevant content even when query wording differs from source text, improving search quality significantly.
A help centre search returns 'reset password' articles when a user types 'cannot log in to my account'.
Embedding
An embedding is a way of representing data — such as words, sentences, or images — as a list of numbers (a vector) in a continuous space. Items that are semantically similar end up close together in this space, allowing machines to understand relationships between concepts.
Vector Database
A vector database is a specialised database designed to store, index, and search high-dimensional vectors (embeddings) efficiently. It enables fast similarity searches — finding items whose vector representations are closest to a given query.
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that enhances a language model's responses by first retrieving relevant information from an external knowledge base, then using that information to generate a more accurate and grounded answer. It combines the strengths of search with generative AI.
Our programme follows a structured Level 4 curriculum with project-based learning, practical workflows, and guided implementation across business and career use cases. Funded route available for UK citizens and ILR holders.