Structural Prompting: Organizing Complex Information

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

Chapter 11 in Prompt Engineering for Accounting and Finance, 2026, pp 379-420 from Springer

Abstract: Abstract This chapter explores structural prompting as an advanced method for organizing complex financial and relational data to enhance AI reasoning accuracy. It introduces graph prompting, structured data prompting, tree prompting, and schema-guided prompting as complementary approaches that make implicit relationships explicit within AI interactions. Through applied accounting and finance scenarios—including fraud network detection, indirect ownership tracing, compliance analysis, financial modeling, and structured reporting—the chapter demonstrates how structured representations reduce ambiguity, improve logical coherence, and enhance transparency. It explains how graphs clarify relational dependencies, structured data formats (JSON, tables, XML) support numerical precision, decision trees guide conditional reasoning, and schema templates ensure output standardization. By aligning prompt structure with the inherently structured nature of accounting data and regulatory frameworks, this chapter positions structural prompting as essential for high-stakes financial environments where auditability, consistency, and compliance are critical.

Keywords: Structural Prompting; Graph Prompting; Tree Prompting; Schema-Guided Prompting; Fraud Detection Networks; Structured Financial Data; AI Transparency in Finance (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_11

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DOI: 10.1007/978-3-032-11195-1_11

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