Retrieval

Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) in external, verified knowledge, injecting real-time data to boost accuracy and eliminate hallucinations.

Retrieval is now synonymous with RAG: a critical AI framework that connects a generative model to an authoritative, external knowledge base. The process is efficient: a user query triggers a semantic search against a vector database, retrieving the most relevant document chunks (embeddings). This retrieved context is then injected into the LLM's prompt, forcing the model to generate a response grounded in specific, up-to-date facts, not just its static training data. This mechanism dramatically improves factual accuracy, reduces the risk of AI hallucination by over 90% in some enterprise applications, and provides verifiable source citations.

https://en.wikipedia.org/wiki/Retrieval-augmented_generation