T-RAG: Lessons from the LLM Trenches

Understand

Large Language Models (LLM) have shown remarkable language capabilities fueling attempts to integrate them into applications across a wide range of domains.

• An important application area is question answering over private enterprise documents where the main considerations are data security, which necessitates applications that can be deployed on-prem, limited computational resources and the need for a robust application that correctly responds to queries.
• Retrieval-Augmented Generation (RAG) has emerged as the most prominent framework for building LLM-based applications.
• While building a RAG is relatively straightforward, making it robust and a reliable application requires extensive customization and relatively deep knowledge of the application domain.