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Prompt Engineering for Finance
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 23m | 70.6 MB
Instructor: Ed Coughlin
Learn how to apply advanced LLM prompt engineering to real-world finance scenarios. This course teaches finance professionals how to use role-based AI personas to improve financial modeling, scenario analysis, decision-making, and reporting at scale.
What you'll learn
As finance teams face increasing pressure to deliver faster insights with fewer resources, prompt engineering has emerged as a critical skill for modern financial professionals. In this course, Prompt Engineering for Finance, you'll gain the ability to use advanced prompt engineering techniques to design, test, and operationalize LLM prompts that mirror real finance workflows.
First, you'll explore building a driver-based forecast including average unit volume, labor costs, food inflation, and real estate expenses for a restaurant chain scenario. Next, you'll discover how to use finance engineering prompts to summarize the expansion forecast results, focusing on EBITDA impact, cash flow timing, and key risks. Then, you'll see how to refine a summary into a one-page executive briefing that highlights decision trade-offs and confidence ranges. Finally, you'll learn how to create a reusable LLM prompt template that can be used each quarter to forecast new restaurant openings by region.
When you're finished with this course, you'll have the skills and knowledge needed to be able to create and operationalize prompt frameworks for reuse, scale, and governance.
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