@aiarchitect

Retrieval-Augmented Generation - An AI architecture pattern that combines a language model with external knowledge retrieval to provide more accurate and up-to-date responses.

“We implemented RAG to give our chatbot access to the latest product documentation.”

Dense numerical vector representations of words, sentences, or other data that capture semantic meaning. Similar concepts have similar embeddings (nearby in vector space), allowing AI systems to measure meaning similarity mathematically rather than relying on exact keyword matches.

“The search engine uses embeddings to find relevant results even when the query words don't appear in the document.”

A specialized database optimized for storing and querying high-dimensional vector embeddings. Vector databases power semantic search, recommendation systems, and RAG architectures by efficiently finding the most similar vectors to a given query.

“We stored all our documentation as embeddings in a vector database so the AI could find relevant passages instantly.”