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Structural Prompting: Organizing Complex Information

In: Prompt Engineering for Accounting and Finance

Author

Listed:
  • Sunil Kumar

    (Roger Williams University)

  • Atreya ‘Chuck’ Chakraborty

    (University of Massachusetts System)

  • Poojan Patel

    (Bryant University)

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.

Suggested Citation

  • Sunil Kumar & Atreya ‘Chuck’ Chakraborty & Poojan Patel, 2026. "Structural Prompting: Organizing Complex Information," Springer Books, in: Prompt Engineering for Accounting and Finance, chapter 11, pages 379-420, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-11195-1_11
    DOI: 10.1007/978-3-032-11195-1_11
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