How Software Engineers Engage with AI: A Pragmatic Process Model and Decision Framework Grounded in Industry Observations

Artificial Intelligence (AI) has the potential to transform Software Engineering (SE) by enhancing productivity, efficiency, and decision support. Tools like GitHub Copilot and ChatGPT have given rise to "vibe coding"-an exploratory, prompt-driven development style. Yet, how software engineers engage with these tools in daily tasks, especially in deciding whether to trust, refine, or reject AI-generated outputs, remains underexplored. This paper presents two complementary contributions. First, a pragmatic process model capturing real-world AI-assisted SE activities, including prompt design, inspection, fallback, and refinement. Second, a 2D decision framework that could help developers reason about trade-offs between effort saved and output quality. Grounded in practitioner reports and direct observations in three industry settings across Turkiye and Azerbaijan, our work illustrates how engineers navigate AI use with human oversight. These models offer structured, lightweight guidance to support more deliberate and effective use of AI tools in SE, contributing to ongoing discussions on practical human-AI collaboration.