Retrieval-Augmented Generation (RAG) for Enterprise Intelligence
Abstract
In today's data-driven world, traditional BI platforms fall short in providing real-time, context-aware insights. Large Language Models (LLMs) like GPT-3 and LLaMA excel in natural language tasks but struggle with outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG) bridges this gap by integrating LLMs with external data sources such as databases, APIs, or vector stores. This chapter explores RAG's architecture, combining retrievers (e.g., FAISS, Elasticsearch) with generators (e.g., GPT, Claude) and its enterprise applications in finance, marketing, HR, and supply chain. Key challenges like data governance, latency, and ethical considerations are discussed, offering a roadmap for deploying RAG to enhance real-time decision-making.
Fields of Study & Subjects
Big Data and Business Intelligence
Data Quality and Management
Big Data and Digital Economy
Publication details
- Journal / source
- Leveraging LLMs for Business Innovation
- Publisher
- IGI Global Scientific Publishing
- Pages
- 69–106
- Publication date
- 5 February 2026
- Language
- NG
- Document type
- Book chapter