Chapter 12: Key Takeaways — Advanced Prompting Techniques

Summary Card

1. Chain-of-thought prompting asks the AI to reason step-by-step before coding. It dramatically improves correctness for complex algorithms and logic-heavy tasks by forcing explicit analysis of inputs, outputs, edge cases, and design decisions before implementation begins.

2. Few-shot prompting teaches the AI your specific patterns by providing two to five concrete examples. It is the most effective technique for ensuring AI-generated code matches your team's coding style, naming conventions, and structural patterns.

3. Role-based prompting activates domain-specific expertise by assigning the AI a professional persona. It works best when the role brings specific knowledge and evaluation criteria — "security engineer" is useful because it activates a checklist of security concerns; "best programmer" is not useful because it specifies nothing concrete.

4. Meta-prompting uses AI to improve your prompts, turning the AI into a prompt engineering assistant. It is particularly powerful for teams: a well-crafted meta-prompt can generate project-specific prompts that help junior developers produce code matching senior developers' standards.

5. Decomposition prompting breaks complex systems into smaller modules that can each be prompted for individually with focused attention. It directly addresses the quality degradation that occurs when AI is asked to generate large, multi-component systems in a single response.

6. Constraint satisfaction prompting makes requirements explicit and verifiable by listing them as categorized constraints (functional, performance, security, code quality). Distinguishing between hard constraints (must be met) and soft constraints (preferred but negotiable) gives the AI clear prioritization.

7. Comparative prompting generates multiple solutions with explicit trade-off analysis, which is invaluable when making design decisions with lasting consequences. Limit comparisons to two or three genuinely different approaches for optimal depth.

8. Socratic prompting inverts the interaction by asking the AI to ask you questions, surfacing hidden requirements and edge cases you would not have discovered on your own. It is the best starting technique when requirements are unclear.

9. Prompt chaining connects multiple prompts in a planned pipeline where each step's output feeds into the next. Prevent context drift by including interface contracts in every step and maintaining a state document that summarizes decisions made so far.

10. A personal prompt library captures your most effective prompts for reuse, bringing consistency, efficiency, and institutional knowledge to your team's AI-assisted development. Treat prompts like code: version them, review them, and improve them over time.

11. Technique selection depends on task characteristics, not on personal preference. Complex algorithms call for chain-of-thought. Pattern matching calls for few-shot. Unclear requirements call for Socratic. Most real-world tasks benefit from combining two or three techniques.

12. The most powerful technique combinations include: Socratic + Decomposition + Chain-of-Thought for complex systems with unclear requirements; Role-Based + Constraint Satisfaction for domain-specific tasks with strict standards; and Few-Shot + Constraint Satisfaction for pattern-consistent code with specific requirements.

13. Avoid over-engineering your prompts. Not every task needs advanced techniques. Simple, well-defined tasks are best served by simple, clear prompts. Advanced techniques add value proportional to the complexity of the task — use them where they help, not everywhere.

14. Start small and build. Begin with one or two techniques for your most common tasks. Master those before expanding your repertoire. A prompt library of three excellent prompts is more valuable than a library of thirty mediocre ones.

Quick Reference: When to Use Each Technique