Chapter 11: Key Takeaways

Iterative Refinement and Conversation Patterns

1. The feedback loop is the fundamental unit of vibe coding. Every productive interaction follows the cycle of prompt, response, and evaluation. The evaluation stage is where your expertise matters most --- it determines the quality of the next prompt.

2. The critique-modify-improve (CMI) cycle provides structure. Rather than giving vague feedback, systematically critique what is wrong, modify what needs to change, and improve what can be elevated. Keep the phases separate for complex changes; combine them for simple ones.

3. Build incrementally, not all at once. Start with the simplest working version and layer complexity on top. The layered approach (data models, then business logic, then error handling, then optimization) catches misunderstandings early before they compound.

4. Avoid the "big bang" anti-pattern. Asking the AI to generate an entire complex system in one prompt produces code that is harder to debug, harder to validate, and more likely to contain compounding errors.

5. Steer with precision when the AI goes off course. Match your steering technique to the degree of divergence: use targeted fixes for small issues, constraint tightening for scope problems, example-driven redirects when words are not working, and partial accepts when some parts are correct.

6. Craft follow-up prompts that are specific, bounded, contextual, and purposeful. Vague prompts ("make it better") waste turns. Effective follow-ups name the function, describe the change, limit the scope, and explain the rationale.

7. Know when code is "good enough" by checking six dimensions. Correctness verified, requirements met, no known bugs, readable code, appropriate error handling, and no obvious security holes. The appropriate quality bar depends on context --- prototypes need fewer iterations than public APIs.

8. Beware the infinite refinement trap. Each iteration has diminishing returns. The jump from broken to working is enormous; the jump from well-structured to perfectly elegant is often negligible in practice. Know which jump you are on.

9. Branch to explore alternatives; backtrack when the current path is a dead end. Not every conversation follows a straight line. Use the explore-compare-decide pattern for important architectural decisions, and do not hesitate to return to a previous working version when the current direction is not productive.

10. Disclose requirements progressively, but foreshadow when architecture depends on future features. Revealing requirements in stages produces better-focused code at each step. But give the AI a heads-up about future needs that would affect current design decisions.

11. Use the AI as a thinking partner, not just a code generator. The rubber duck effect --- explaining your system to the AI --- often reveals design flaws and hidden assumptions. Ask the AI to challenge your thinking, explore tradeoffs, and pressure-test your solutions.

12. Scale iteration for complex systems with a multi-phase workflow. Architecture conversation first, then foundation building, then a vertical slice, then horizontal expansion, then cross-cutting concerns, then hardening. Each phase uses all the iteration techniques from this chapter.

13. Track your iteration metrics to improve over time. Iterations per feature, backtrack rate, time to "good enough," and common correction types all provide data that helps you refine your own prompting skills.

14. Manage conversation length actively. Long conversations can confuse the AI with contradictory context from earlier turns. When quality degrades, start a fresh conversation with a condensed summary of decisions and current state.

15. The core insight: vibe coding is about the conversation, not the prompt. The initial prompt is just the beginning. The real skill lies in what you do with the response --- how you evaluate, steer, refine, and build through multiple turns of skilled dialogue.