Pathway 11: "I'm a Domain Expert Applying LLMs" (Healthcare / Legal / Finance Professional)
Target audience: Healthcare, legal, finance, and other domain professionals with deep expertise but limited ML background
Goal: Build LLM applications tailored to your domain while navigating regulatory requirements, ensuring accuracy, and integrating with existing professional workflows.
Chapter Guide
- Start Ch 00: ML and PyTorch Foundations (if unfamiliar with ML concepts) review ML basics if unfamiliar
- Focus Ch 10: Working with LLM APIs (your practical starting point) your practical starting point with LLMs
- Focus Ch 11: Prompt Engineering write prompts tailored to your domain
- Focus Ch 12: Hybrid ML+LLM Architectures combine domain models with LLM reasoning
- Skim Ch 14: Fine-Tuning Fundamentals consider fine-tuning for domain-specific tasks
- Focus Ch 19: Embeddings and Vector Databases index your domain documents for retrieval
- Focus Ch 20: RAG ground LLM answers in your domain knowledge
- Focus Ch 21: Conversational AI build chatbots for your domain users
- Skim Ch 25: Specialized Agents (domain-specific agent patterns) agent patterns for healthcare, legal, finance
- Focus Ch 28: LLM Applications applied patterns relevant to your domain
- Skim Ch 29: Evaluation and Experiment Design measure accuracy for your domain's standards
- Focus Ch 32: Safety, Ethics and Compliance regulatory compliance for your industry
- Skim Ch 33: Strategy, Product and ROI build the business case in your organization
- Optional Ch 34: Emerging Architectures upcoming models relevant to domain applications
- Skim Ch 35: AI and Society governance and labor market implications for your industry
Recommended Appendices
- Appendix I: Prompt Templates – reusable prompt templates for domain tasks
- Appendix V: Tooling Ecosystem – survey tools for integrating LLMs into your workflow
What Comes Next
Return to the Reading Pathways overview to explore other pathways, or proceed to FM.4: How to Use This Book for a quick orientation on conventions and callout types, then start reading.