A Chain-of-Thought Prompt Engineering Framework for Automated Technical Specification Generation in Enterprise Integration: A Real-World Case Study

Abstract

Technical Specification Documents (TSDs) translate functional requirements into implementable designs and are mandatory artifacts in governed Enterprise Application Integration (EAI) programs, yet their production remains a manual, expertise-intensive bottleneck. Large Language Models (LLMs) have been studied extensively for code generation, but long-form, template-governed engineering documents remain comparatively unexplored. This paper reports an industrial case study, conducted during a migration of more than 250 enterprise integrations to Microsoft Azure Integration Services at a large Australian wholesale distributor, that introduces a chain-of-thought prompt-engineering framework for automated TSD generation using GitHub Copilot. The framework combines a two-phase generation architecture that grounds the model in the functional specification, organizational standards, and the approved template; a six-stage prompting pipeline with human validation checkpoints; and Prompt-as-a-Code, under which prompts are version-controlled and reviewed as engineering artifacts. Applied to a stratified sample of fifteen production interfaces, the framework reduced TSD authoring effort from 793 to 300 person-hours, a 62.2% pooled reduction and a 64.0% mean per-document reduction (Wilcoxon signed-rank, $n = 15, p \lt 0.001$ ), at 74.3% reviewer-assessed accuracy and 3.7/5 developer satisfaction. Benefits degrade monotonically with interface complexity: between the simplest and most complex bands, effort reduction falls from 70.6% to 58.9% and accuracy from 91% to 62.5%, and the residual human effort concentrates in diagrams, sequence flows, and cross-system integration content. We additionally report qualitative observations of GPT-4.1/5 and Claude Sonnet 4, as well as Copilot’s Ask and Agent interaction modes, offering reusable, practice-grounded guidance for enterprise settings.