ERATTA: Extreme RAG for Table To Answers with Large Language Models
This work addresses scalability and reliability issues in RAG-LLM systems for enterprise data practices, though it appears incremental as it builds on existing RAG and LLM methods.
The authors tackled the problem of unreliable and costly agentic-RAG for enterprise table data by proposing a multi-LLM system that enables structured question-answering in under 10 seconds per query, achieving over 90% confidence scores across hundreds of queries in domains like sustainability and finance.
Large language models (LLMs) with retrieval augmented-generation (RAG) have been the optimal choice for scalable generative AI solutions in the recent past. Although RAG implemented with AI agents (agentic-RAG) has been recently popularized, its suffers from unstable cost and unreliable performances for Enterprise-level data-practices. Most existing use-cases that incorporate RAG with LLMs have been either generic or extremely domain specific, thereby questioning the scalability and generalizability of RAG-LLM approaches. In this work, we propose a unique LLM-based system where multiple LLMs can be invoked to enable data authentication, user-query routing, data-retrieval and custom prompting for question-answering capabilities from Enterprise-data tables. The source tables here are highly fluctuating and large in size and the proposed framework enables structured responses in under 10 seconds per query. Additionally, we propose a five metric scoring module that detects and reports hallucinations in the LLM responses. Our proposed system and scoring metrics achieve >90% confidence scores across hundreds of user queries in the sustainability, financial health and social media domains. Extensions to the proposed extreme RAG architectures can enable heterogeneous source querying using LLMs.