FM.3.T5: Domain Specialist Track

Python basics. Domain expertise in healthcare, legal, or finance. Chapters 10 through 11 for LLM API fundamentals. No deep ML knowledge required.

Domain Specialist Track

Applying LLMs to domain problems (medical, legal, business, education) without requiring deep ML expertise.

Learning Sequence

Follow the numbered steps in order. Each step builds on the previous one to give you a coherent understanding of this topic area.

Chapter 00: ML and PyTorch Foundations (skim for vocabulary; skip the code if needed)
Chapter 10: Working with LLM APIs (the practical starting point for using LLMs)
Chapter 11: Prompt Engineering and Advanced Techniques (getting high-quality outputs for your domain)
Chapter 20: Retrieval-Augmented Generation (connecting LLMs to your organization's data)
Chapter 28: LLM Applications (code generation, summarization, search, recommendations)
Chapter 33: LLM Strategy, Product Management and ROI (making the business case, vendor selection, cost planning)
Chapter 34: Emerging Architectures and Scaling Frontiers (emerging models and architectures relevant to domain applications)
Chapter 35: AI, Society and Open Problems (AI governance, labor markets, and industry-specific implications)

Recommended Appendices

Appendix I: Prompt Templates – reusable prompt templates for domain tasks
Appendix V: Tooling Ecosystem – survey tools for integrating LLMs into your domain
Appendix D: Environment Setup – set up your working environment quickly

Return to the Course Syllabi overview to explore other tracks and courses, or proceed to FM.4: How to Use This Book for a quick orientation on conventions and callout types.