Business insights using RAG–LLMs: a review and case study

Checked 1 Oct 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.

The sources disagree. The gate chose the strictest class (orange). A human review set it to green: “The publisher's Crossref record gives CC-BY 4.0 for the version of record; OpenAlex and Unpaywall only report other-oa. Reviewed by Fredrik 2026-09-30.”

As organizations increasingly rely on diverse data sources like invoices and surveys, efficient Information Extraction (IE) is crucial. Natural Language Processing (NLP) enhances IE through tasks such as Named Entity Recognition (NER), Relation Extraction (RE), Event Extraction (EE), Term Extraction (TE), and Topic Modeling (TM). However, implementing these methods requires significant expertise, which smaller organizations often lack. Large Language Models (LLMs), powered by Generative Artificial Intelligence (GenAI), can address this by performing multiple IE tasks without extensive development costs. However, LLMs may struggle with domain-specific accuracy. Integrating Retrieval-Augmented Generation (RAG) with LLMs improves precision by incorporating external data. Despite the potential, research on RAG-LLM applications in the business domain is limited. This article reviews Business IE systems, explores RAG-LLM applications across disciplines, and presents a case study demonstrating how RAG-LLMs can enhance business insights, offering scalable, cost-effective solutions.

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• RAG reliability and evaluation