Fundamentals of Prompt Engineering

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 3 in Prompt Engineering for Accounting and Finance, 2026, pp 75-102 from Springer

Abstract: Abstract This chapter introduces the fundamental principles of prompt engineering as a structured methodology for guiding generative AI in accounting and finance contexts. It explains how large language models process prompts through tokenization, probabilistic pattern recognition, and context-window constraints, emphasizing that AI predicts outputs rather than truly understanding financial concepts. The chapter establishes four core principles—clarity, relevance, conciseness, and iterative refinement—and demonstrates how each enhances accuracy, compliance alignment, and analytical precision. Through applied financial examples, it shows how prompt structure directly influences output quality, especially in tasks involving regulatory standards, ratio analysis, valuation, and forecasting. The chapter also introduces the input–output relationship, illustrating how carefully constructed prompts yield context-specific, defensible responses, whereas vague prompts produce superficial answers. By integrating technical AI mechanics with practical financial applications, this chapter positions prompt engineering as a foundational professional skill that transforms AI from a generic text generator into a structured analytical assistant.

Keywords: Prompt Engineering; Large Language Models; Input–Output Relationship; AI Tokenization; Financial Analysis Prompts; Regulatory Compliance and AI; Iterative Refinement (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_3

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DOI: 10.1007/978-3-032-11195-1_3

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