Chapter 11: Prompt Engineering for AI-Driven Development
Clear prompts get better AI responses. Vague prompts waste time with back-and-forth clarification. This chapter teaches you how to communicate your intent effectively so AI can help you accomplish development tasks.
You'll learn to structure prompts clearly, iterate through collaboration with AI, validate outputs systematically, and build reusable prompt templates for recurring tasks. By the end of this chapter, you'll have your own prompt toolkit—a collection of tested templates you'll use in daily development work.
🎯 Before You Begin
What You'll Learn
This chapter focuses exclusively on what you SAY to your AI agent (prompt engineering). Chapter 16 will teach what your AI agent KNOWS when you say it (context engineering). By the end of this chapter, you'll have:
- A Personal Prompt Toolkit: 5-7 tested templates for recurring development tasks (git commits, bash debugging, markdown documentation, etc.)
- A Decision Guide: Clear mapping from tasks to templates, so you know which template to use when
- Validated Templates: Each template tested on real tasks and refined based on results
- Reusable Skills: Patterns you'll apply throughout the book and in professional development
- Task + Context + Format: The three-element structure for effective prompts
- Progressive Prompting: Anthropic's approach (simple → structured → examples → reasoning)
- Context Completeness: Google's emphasis on rich environmental context for better AI outputs
- Constraint-Based Iteration: OpenAI's incremental refinement approach (add constraints one at a time)
- Iterative Refinement: Working with AI through multiple rounds to improve results
- Question-Driven Development: Prompting AI to ask clarifying questions before solving problems
- Decision Guide: Mapping common tasks to specific templates for fast selection