Evaluating AI-generated Solutions

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

Chapter 12 in Prompt Engineering for Accounting and Finance, 2026, pp 421-451 from Springer

Abstract: 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.

Keywords: AI Evaluation Frameworks; Hallucination Detection; Quantitative vs Qualitative Assessment; AI in Financial Decision-Making; Error Typologies; Human-AI Oversight; AI Governance in Accounting (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_12

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DOI: 10.1007/978-3-032-11195-1_12

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