🔗 ← Class 6: Commercial LLM Platforms | Module 2: Platforms and Tools →

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Resources

📄 Lesson PDF

📊 Slides PDF

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Video Lesson

https://youtu.be/MK8M5lnaPNk

Learning Objectives

By the end of this lesson, participants will be able to:

  1. Explain the logical gap between human abductive reasoning and AI inductive pattern recognition, demonstrating why prompts serve as essential translation interfaces rather than simple questions
  2. Apply the P-T-C-F framework (Persona, Task, Context, Format) to structure effective prompts for military applications, drawing direct parallels to five-paragraph OPORD construction
  3. Configure LLM parameters including temperature, top-p, and top-k to control output characteristics for different task requirements ranging from deterministic fact extraction to creative brainstorming
  4. Select appropriate prompting techniques from zero-shot through chain-of-thought based on task complexity, distinguishing when simple instructions suffice versus when advanced reasoning techniques justify additional token costs
  5. Evaluate when to use advanced techniques including Retrieval-Augmented Generation, ReAct, and Tree of Thoughts for specialized applications requiring external knowledge integration or multi-path exploration

Introduction

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