Generative AI is reshaping software engineering. As models like ChatGPT, Copilot, Claude, and Gemini generate code, your competitive advantage shifts from coding to analysis—clearly defining what software must do at the right level of abstraction. This course outlines the essentials of generative analysis, a practical, repeatable approach that bridges business analysis and engineering to produce high-quality inputs for LLMs and reliable outputs for your systems. Learn how to make sound decisions about abstraction, model with UML, apply the Unified Process framework in an AI-augmented workflow, and use literate modeling to create narrative-rich specifications. Along the way, find out how to use M++ to fact-check and refine AI output using precise language patterns and multivalent logic. This course gives you a hands-on opportunity to practice prompt engineering, concept and dialog mapping, use case modeling, class and architecture design, requirements processing, and more.
This course was created by Pearson and Jim Arlow. We are pleased to host this training in our library.
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