Pathway 16: "I Want to Become a Prompt Engineer Specialist" (Prompt Engineer)

Target audience: Developers, analysts, and content professionals who want to specialize in crafting effective prompts, building structured output pipelines, and mastering LLM API integration

Goal: Develop deep expertise in prompt engineering techniques (few-shot, chain-of-thought, tool use, DSPy), structured output formats, and LLM API patterns for building reliable, reproducible LLM workflows.

Chapter Guide

• Skim Ch 05: Decoding and Text Generation (understand temperature, top-p, and how sampling affects outputs) understand how temperature and sampling shape output
• Skim Ch 07: The Modern LLM Landscape (know which models to choose and why) know which models to choose for each task
• Skim Ch 08: Reasoning Models (when to use reasoning models vs. standard models) when reasoning models outperform standard prompts
• Focus Ch 10: Working with LLM APIs (structured outputs, streaming, multi-provider patterns) structured outputs, streaming, multi-provider calls
• Focus Ch 11: Prompt Engineering (your core chapter: CoT, few-shot, DSPy, prompt optimization) your core chapter: CoT, few-shot, DSPy, optimization
• Focus Ch 12: Hybrid ML+LLM Architectures (routing, cascading, fallback patterns) routing, cascading, and fallback prompt strategies
• Focus Ch 20: RAG (prompt engineering for retrieval-augmented contexts) prompt design for retrieval-augmented contexts
• Skim Ch 22: AI Agents (system prompts and agent instructions) craft system prompts for agent behavior
• Focus Ch 29: Evaluation (measuring prompt quality systematically) measure prompt quality with systematic evaluation
• Skim Ch 32: Safety (prompt injection defense, guardrails) defend against prompt injection and misuse
• Optional Ch 34: Emerging Architectures new model designs that affect prompting strategies
• Optional Ch 35: AI and Society responsible prompting and societal context