Adaptive AI: Modifying Model Behavior with Prompts

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 8 in Prompt Engineering for Accounting and Finance, 2026, pp 257-294 from Springer

Abstract: Abstract This chapter introduces adaptive prompting as an advanced framework for enabling AI systems to iteratively refine their responses through feedback loops. It contrasts adaptive techniques with static prompting approaches and explains how ReAct (Reasoning + Acting) and Reflexion prompting enhance accuracy, reliability, and depth of analysis. ReAct integrates reasoning with external actions such as data retrieval or policy lookup, grounding responses in real-time or authoritative information. Reflexion prompting introduces self-evaluation cycles in which AI critiques and revises its own outputs to correct logical errors or omissions. Through applied examples in auditing, reconciliation, valuation, lease classification, and investment analysis, the chapter demonstrates how adaptive methods reduce hallucinations, improve compliance alignment, and strengthen professional trust in AI outputs. By embedding verification, external evidence integration, and iterative refinement into the prompting process, adaptive prompting aligns AI behavior more closely with professional analytical practices in accounting and finance.

Keywords: Adaptive Prompting; ReAct Prompting; Reflexion Prompting; Iterative AI Reasoning; AI Error Correction; Real-Time Financial Data Integration; Self-Evaluating AI Systems (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_8

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DOI: 10.1007/978-3-032-11195-1_8

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