Retrieval-Augmented Generation
Also known as: RAG
A technique that enhances AI model outputs by retrieving relevant information from external knowledge sources and incorporating it into the model's context before generating a response.
Sources & References
Related Terms
Context Window
The maximum amount of text (measured in tokens) that a large language model can process in a single interaction, encompassing both the input prompt and the generated output. Managing context windows effectively is critical for enterprise AI deployments where complex queries require extensive background information.
Embeddings
Dense numerical vector representations of data (text, images, audio) that capture semantic meaning, enabling similarity comparisons and machine learning operations in a continuous vector space.
Knowledge Base
A structured repository of information, facts, and relationships used by AI systems as a source of context and ground truth for answering queries and making decisions.
Large Language Model
A type of AI model trained on vast amounts of text data that can understand, generate, and manipulate human language, typically based on the transformer architecture with billions of parameters.
Vector Database
A specialized database designed to store, index, and query high-dimensional vector embeddings, enabling efficient similarity search used in RAG systems and AI applications.