Module Overview
This module treats prompting as an engineering discipline. It deconstructs prompt anatomy, surveys core and advanced techniques, and covers the testing and refinement loops that make prompts robust rather than brittle.
Learning Objectives
- Deconstruct a prompt into instruction, context, input, and output format.
- Apply zero-, one-, and few-shot prompting and system-prompt role assignment.
- Use chain-of-thought and step-back prompting and enforce structured output.
- Test prompt sensitivity, fragility, and robustness.
- Apply chaining, meta-prompting, and self-refinement loops.
Topics Covered
Prompt Anatomy & Basic Techniques
- Anatomy of a prompt (instruction, context, input, output format)
- Zero-shot, one-shot, and few-shot prompting
- System prompt design and role assignment
Advanced Reasoning & Structured Output
- Chain-of-thought and step-back prompting
- Output formatting and structured generation (JSON mode, XML tags)
Prompt Testing, Chaining & Self-Refinement
- Prompt sensitivity, fragility, and robustness testing
- Prompt chaining and decomposition strategies
- Meta-prompting and self-refinement loops
Key Concepts & Terminology
Few-shot exemplars, role/system prompt, chain-of-thought, step-back abstraction, structured generation, prompt robustness, decomposition, self-refinement.
Tools & Frameworks Referenced
Structured-output / JSON-mode interfaces; prompt-testing harnesses (covered in Module 24).
Prerequisites
Basic LLM application experience; complements Module 23 (context engineering).