Deploying Large Language Models With Retrieval Augmented Generation
Knowing that the generative capabilities of large language models (LLM) are sometimes hampered by tendencies to hallucinate or create non-factual responses, researchers have increasingly focused on methods to ground generated outputs in factual data. Retrieval Augmented Generation (RAG) has emerged as a key approach for integrating knowledge from data sources outside of the LLM's training set, including proprietary and up-to-date information. While many research papers explore various RAG strategies, their true efficacy is tested in real-world applications with actual data. The journey from conceiving an idea to actualizing it in the real world is a lengthy process. We present insights from the development and field-testing of a pilot project that integrates LLMs with RAG for information retrieval. Additionally, we examine the impacts on the information value chain, encompassing people, processes, and technology. Our aim is to identify the opportunities and challenges of implementing this emerging technology, particularly within the context of behavioral research in the information systems (IS) field. The contributions of this work include the development of best practices and recommendations for adopting this promising technology while ensuring compliance with industry regulations through a proposed AI governance model.
Code (1)
Tasks
Information RetrievalRAGRetrievalRetrieval-augmented GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Searching for Best Practices in Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains. While man…
Question AnsweringRAGRetrievalRetrieval-augmented GenerationModel-Driven Quantum Code Generation Using Large Language Models and Retrieval-Augmented Generation
This paper introduces a novel research direction for model-to-text/code transformations by leveraging Large Language Models (LLMs) that can be enhanced with Retrieval-Augmented Generation (RAG) pipelines. The focus is on…
Code GenerationRAGe: A Retrieval-Augmented Generation Evaluation Framework
Deploying Large Language Model (LLM) applications, particularly those relying on Retrieval-Augmented Generation (RAG), remains challenging due to high computational demands, outdated knowledge bases, and the need to manu…
Reducing hallucination in structured outputs via Retrieval-Augmented Generation
A common and fundamental limitation of Generative AI (GenAI) is its propensity to hallucinate. While large language models (LLM) have taken the world by storm, without eliminating or at least reducing hallucinations, rea…
HallucinationRAGRetrievalRetrieval-augmented GenerationRAG Does Not Work for Enterprises
Retrieval-Augmented Generation (RAG) improves the accuracy and relevance of large language model outputs by incorporating knowledge retrieval. However, implementing RAG in enterprises poses challenges around data securit…
Language ModelingLanguage ModellingLarge Language ModelRAG+2