Enhancing AI Reasoning: Cognitive Techniques

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 5 in Prompt Engineering for Accounting and Finance, 2026, pp 133-177 from Springer

Abstract: Abstract This chapter advances beyond foundational prompting methods to introduce cognitive prompting techniques designed to enhance AI reasoning depth, reliability, and interpretability in accounting and finance contexts. It presents three structured approaches—Chain-of-Thought, Tree-of-Thought, and Self-Consistency prompting—that emulate human analytical reasoning by guiding AI through sequential logic, branching scenario evaluation, and cross-verification of conclusions. Through detailed financial applications in capital budgeting, tax planning, financial reporting, forecasting, and audit analysis, the chapter demonstrates how these techniques reduce superficial outputs and mitigate variability in AI-generated results. The comparative analysis between simple and cognitive prompting methods highlights improvements in transparency, analytical rigor, and decision confidence. By encouraging structured reasoning and multi-path validation, these techniques transform AI from a surface-level responder into a deliberative analytical assistant. The chapter positions cognitive prompting as essential for high-stakes financial environments where precision, auditability, and defensible logic are critical.

Keywords: Chain-of-Thought Prompting; Tree-of-Thought Prompting; Self-Consistency Prompting; AI Reasoning in Finance; Structured Analytical AI; Decision-Tree Analysis; Financial Scenario Modeling (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-11195-1_5

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DOI: 10.1007/978-3-032-11195-1_5

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