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Task-oriented Prompting: Structuring AI Workflows

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 develops task-oriented prompting as a methodology for structuring AI interactions around multi-step workflows common in accounting and finance. Moving beyond single-turn responses, it introduces prompt chaining and meta prompting as techniques for decomposing complex tasks into sequenced or adaptive processes. Prompt chaining enables structured execution of defined steps such as reconciliation, valuation modeling, audit testing, and financial forecasting, improving transparency and reliability through intermediate outputs. Meta prompting extends this approach by allowing AI systems to dynamically propose or refine prompts, enhancing adaptability in evolving or exploratory tasks. Through applied examples in financial analysis, audit planning, consolidation, and scenario forecasting, the chapter demonstrates how structured workflows reduce logical gaps, improve oversight, and strengthen decision support. By positioning AI as a collaborative workflow assistant rather than a static question-answering tool, this chapter establishes process control and structured reasoning as central competencies in professional AI use.

Suggested Citation

  • Sunil Kumar & Atreya ‘Chuck’ Chakraborty & Poojan Patel, 2026. "Task-oriented Prompting: Structuring AI Workflows," Springer Books, in: Prompt Engineering for Accounting and Finance, chapter 7, pages 209-255, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-11195-1_7
    DOI: 10.1007/978-3-032-11195-1_7
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