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Automating and Optimizing Prompts: ­ AI-driven Efficiency

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 examines prompt automation and optimization as a strategy for improving AI reliability, scalability, and efficiency in accounting and finance workflows. It explains how automated prompt engineering techniques—including auto-prompting and active prompting—shift prompt refinement from manual trial-and-error to structured, feedback-driven processes. Auto-prompting enables AI systems to generate, test, and select optimized prompt variations, improving consistency and reducing human intervention. Active prompting, inspired by active learning, focuses on identifying model weaknesses and refining prompts through targeted examples that address uncertain or error-prone cases. Through applied scenarios in expense auditing, valuation analysis, revenue recognition, and financial reporting, the chapter demonstrates how optimized prompts enhance compliance accuracy, reduce hallucinations, and improve decision support. By treating prompts as adaptive, optimizable components rather than static instructions, this chapter positions prompt optimization as a foundational mechanism for scaling trustworthy AI applications in professional finance environments.

Suggested Citation

  • Sunil Kumar & Atreya ‘Chuck’ Chakraborty & Poojan Patel, 2026. "Automating and Optimizing Prompts: ­ AI-driven Efficiency," Springer Books, in: Prompt Engineering for Accounting and Finance, chapter 9, pages 295-339, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-11195-1_9
    DOI: 10.1007/978-3-032-11195-1_9
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