Knowledge-driven Prompting: Integrating Context for Better Responses

Sunil Kumar (), Atreya ‘Chuck’ Chakraborty () and Poojan Patel ()
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Sunil Kumar: Roger Williams University
Atreya ‘Chuck’ Chakraborty: University of Massachusetts System
Poojan Patel: Bryant University

Chapter 6 in Prompt Engineering for Accounting and Finance, 2026, pp 179-207 from Springer

Abstract: Abstract This chapter introduces knowledge-driven prompting as a structured approach for grounding AI outputs in authoritative and up-to-date information. It explains the limitations of reasoning-only methods and demonstrates how supplying external knowledge significantly improves factual accuracy in accounting and finance contexts. Two core techniques are examined: General Knowledge Prompting (GKP), where users manually embed relevant standards, rules, or data into prompts, and Retrieval-Augmented Generation (RAG), where AI systems automatically retrieve and integrate external sources such as accounting standards, tax regulations, or financial reports. Through applied scenarios in revenue recognition, lease accounting, tax law updates, and financial analysis, the chapter illustrates how knowledge integration reduces hallucinations, enhances compliance, and strengthens analytical reliability. It further compares knowledge-driven methods with foundational and cognitive prompting techniques, positioning external information access as a third pillar of prompt engineering. The chapter concludes that in knowledge-intensive domains like accounting and finance, AI must operate as an “open-book” assistant to ensure precision, transparency, and regulatory alignment.

Keywords: Knowledge-Driven Prompting; Retrieval-Augmented Generation; General Knowledge Prompting; AI in Accounting Standards; Regulatory Compliance and AI; External Data Integration; Hallucination Reduction (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-11195-1_6

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