About this document

05 RAG Retrieval Augmented Generation by jarharsh11 is a document available to read on EtoBox.

RAG (Retrieval-Augmented Generation) enhances AI systems by integrating real-time enterprise data, addressing limitations of standard LLMs such as knowledge cutoffs and hallucinations. It utilizes an indexing and query pipeline for efficient retrieval and context-aware responses, improving accuracy, attribution, and freshness of information. The system is designed for applications like healthcare marketing, enabling organizations to leverage their proprietary knowledge effectively.

Author

jarharsh11

Language

EN