Enterprise chatbots powered by generative AI are rapidly emerging as promising tools to enhance employee productivity. Key technological components for building these chatbots include Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex. However, the creation of successful enterprise chatbots poses significant challenges, primarily due to the meticulous engineering required for RAG pipelines.
In a new paper FACTS About Building Retrieval Augmented Generation-based Chatbots, an NVIDIA research team introduces the FACTS framework, designed to create robust, secure, and enterprise-grade RAG-based chatbots.
The FACTS mnemonic represents five critical dimensions for these chatbots: content freshness (F), architectures (A), cost economics of LLMs (C), testing (T), and security (S). This framework is built upon the team’s experience developing three chatbots at NVIDIA, including those for IT and HR benefits, company financial earnings, and general enterprise content.
The researchers identify and elaborate on fifteen critical control points within RAG pipelines, providing strategies to enhance chatbot performance at each stage. They also offer practical techniques for handling complex queries, testing, and ensuring security. Additionally, the paper presents a reference architecture for implementing agentic architectures for complex query handling, strategies for testing and evaluating subjective query responses, and guidelines for managing document access control lists (ACLs) and security.
Furthermore, the paper introduces a reference architecture for a flexible generative-AI based chatbot platform. This work provides a comprehensive perspective on essential factors and practical solutions for building secure and efficient enterprise-grade chatbots, making a significant contribution to the field. Empirical results demonstrate that the resulting chatbots are effective, secure, and cost-efficient.
The paper FACTS About Building Retrieval Augmented Generation-based Chatbots is on arXiv.
Author: Hecate He | Editor: Chain Zhang
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The way of deploying and analyzing the problem is very detailed and profound, demonstrating the author’s deep understanding. It’s great strands hint
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