ERATTA: Extreme RAG for Table To Answers with Large Language Models discusses how Large Language Models (LLMs) with residual augmented-generation (RAG) can be integral for efficiently handling data tables of varying sizes and complexities. This new system allows for real-time responses and incorporates a user authentication process, making it ideal for enterprise-level data operations.

• Capable of handling user queries in under 10 seconds
• Enhanced data retrieval and custom prompting for natural language responses
• Features a scoring system to detect and correct LLM hallucinations
• Demonstrates over 90% confidence scores in user interactions
• Offers potential for extending to heterogeneous source querying

This research showcases the potential of combining RAG with LLMs in creating more scalable and domain agnostic AI solutions. Its architecture could revolutionize data handling in many sectors, notably in areas requiring quick, reliable information retrieval.