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Evaluating AI-generated Solutions

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 establishes structured frameworks for critically evaluating AI-generated outputs in accounting and finance contexts. It examines the strengths and limitations of generative AI, including efficiency gains, knowledge breadth, hallucination risk, logical errors, and overconfidence. The chapter introduces practical evaluation methodologies such as structured rubrics, error typologies, benchmarking, cross-verification techniques, and human-AI review checklists to assess accuracy, completeness, reasoning quality, and compliance alignment. It differentiates quantitative evaluation approaches (accuracy, precision, recall, and error rates) from qualitative expert judgment, emphasizing their complementary roles in high-stakes financial decision-making. The chapter also demonstrates how prompt design influences evaluation criteria, requiring tailored review strategies for chain-of-thought, role-based, and structured prompts. By integrating systematic evaluation frameworks into AI-assisted workflows, this chapter positions critical assessment as a core professional responsibility in ensuring reliable, defensible, and ethically sound financial outcomes.

Suggested Citation

  • Sunil Kumar & Atreya ‘Chuck’ Chakraborty & Poojan Patel, 2026. "Evaluating AI-generated Solutions," Springer Books, in: Prompt Engineering for Accounting and Finance, chapter 12, pages 421-451, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-11195-1_12
    DOI: 10.1007/978-3-032-11195-1_12
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