Module 10: Prompt Engineering

Prompt engineering is the practice of designing, testing, and optimizing the instructions you give to language models. It sits at the intersection of software engineering and cognitive science - how you frame a task fundamentally changes what the model produces.

This module covers prompt engineering from first principles through production-grade practices.

What You'll Learn

Lessons

#	Lesson	Key Concepts
01	Prompt Design Fundamentals	Clarity, specificity, task framing, role assignment
02	System Prompts and Personas	Persona design, constraint setting, system prompt anatomy
03	Few-Shot and Chain-of-Thought	Example selection, CoT formats, self-consistency
04	Prompt Templates and Composition	Jinja2 templates, variable injection, modular prompts
05	Prompt Versioning and Management	Prompt-as-code, semantic versioning, A/B testing
06	Prompt Injection Defense	Attack taxonomy, defense layers, sanitization
07	Prompt Debugging Methodology	Isolation, ablation, systematic root cause
08	Advanced Prompting Techniques	Self-refinement, Tree of Thought, ReAct, meta-prompting

Prerequisites

• Familiarity with LLM APIs (Module 01)
• Basic Python and API usage

The Core Challenge

Prompts are executable instructions for non-deterministic systems. Unlike traditional code where the same input always produces the same output, LLMs are probabilistic. A prompt that works 95% of the time fails 5% of the time - and those failures are often surprising and difficult to debug. Production prompt engineering is about reducing that failure rate systematically.