Large Language Models (LLMs) have revolutionized natural language processing, enabling new applications in enterprise knowledge management. This whitepaper explores the implementation of Retrieval-Augmented Generation (RAG) systems, which combine existing knowledge bases with LLMs to enable natural language querying of internal data.

We address key challenges in developing and deploying production-grade RAG systems discussing:

• RAGs and Vector Databases: An overview of these technologies and their role in transforming data querying and knowledge retrieval processes within organizations.
• Large Language Model Integration: Strategies for effectively incorporating LLMs into existing knowledge bases, including best practices for data processing and embedding.
• Computational Requirements: An analysis of GPU compute needs for various scales of implementation, enabling informed decision-making on infrastructure investments.