Prompt Engineering for Systems Analysts

Systems and business analysts spend countless hours drafting requirements, user stories, and technical specifications. Generative AI can significantly streamline this process, but only if you know how to structure your requests. This course teaches you how to leverage large language models (LLMs) to accelerate your analytical workflow and improve documentation quality. You will start with key terminology and foundational concepts of how AI processes information. By mastering how to communicate with these models, you can transform raw stakeholder interviews and vague ideas into highly structured, professional documentation. What you'll learn: • Understand the foundational concepts of large language models and context window management. • Apply modern techniques like chain-of-thought and few-shot prompting to generate accurate requirements. • Create structured technical specifications and user stories from unstructured stakeholder input. • Practice drafting precise acceptance criteria and use cases using iterative refinement prompts. • Implement basic Retrieval-Augmented Generation (RAG) patterns to ground AI outputs in your existing project data. • Design reusable prompt templates for consistent, high-quality business analysis artifacts. The course flows from basic AI definitions to advanced, role-specific prompting strategies. You will read through practical examples and text-based exercises that simulate real-world analyst scenarios, ensuring you can immediately apply these skills to your daily tasks. Designed for beginner systems analysts, business analysts, and product managers, this course requires no prior AI or technical experience. Start reading today to optimize your documentation workflow and become a more effective analyst.