Chapter 6: Key Takeaways

Your First Vibe Coding Session --- Summary Card

Start every vibe coding session with a planning prompt. Describe your project goals, desired features, and ask the AI to help plan the architecture. This gives the AI context that improves all subsequent code generation and gives you a chance to evaluate the AI's understanding before writing code.
Narrow the scope before generating code. The AI will often suggest more features than you need. Consciously decide what to build now and what to defer. Starting small and expanding incrementally produces more reliable results than trying to build everything at once.
Use specification prompts for predictable output. When you know exactly what you want --- specific function names, parameter types, return values, and behaviors --- state them explicitly. Vague prompts produce inconsistent results; specific prompts get you the code you want on the first try.
Show examples for formatting. When you want specific output formatting, display layouts, or text patterns, showing the AI a concrete example of the desired output is the most effective technique. The AI can reverse-engineer formatting rules from examples more reliably than from descriptions.
Iteration is the norm, not a failure. Expect to send follow-up prompts to refine, fix, and extend the AI's output. The vibe coding workflow is inherently iterative: generate, evaluate, refine, repeat. Each iteration improves the code.
You must think about edge cases and error handling. AI assistants tend to generate "happy path" code that works correctly with valid inputs but fails on edge cases. It is your responsibility to identify potential failure scenarios (corrupted data, missing files, invalid input) and explicitly ask the AI to handle them.
Use dependency injection for testability. Accept configuration values (like file paths) as function parameters with sensible defaults rather than hardcoding them. This makes your code testable with temporary files and flexible enough to work in different environments.
Test AI-generated code thoroughly. Testing is more important with AI-generated code than hand-written code because the AI may introduce subtle assumptions or edge case failures that you did not anticipate. Specify concrete test scenarios in your prompts to get meaningful tests.
Read and evaluate every piece of generated code. You do not need to understand every syntax detail, but you should understand what each function does, what data it expects, and what it returns. Ask the AI to explain anything that is unclear.
The vibe coding workflow is: Plan, Generate, Evaluate, Iterate, Test, Reflect. This five-to-six step loop applies to projects of any size. Planning gives context, generation produces code, evaluation catches issues, iteration improves quality, testing verifies correctness, and reflection identifies next steps.
Python dataclasses are ideal for data models. They provide a clean, typed, self-documenting way to define the shape of your data. Combined with asdict() for serialization and **kwargs for deserialization, they integrate naturally with JSON persistence.
Atomic writes prevent data corruption. When saving data to disk, write to a temporary file first and then rename it. This ensures the original file is never left in a partially written state if the program crashes during the write.
Use the AI as a reviewer after building. Once your application is working, ask the AI what it would change to make the code more production-ready. This surfaces improvements and blind spots you might have missed.
Vibe coding works for complete beginners. As demonstrated by the case studies, people with no programming experience can build functional applications through vibe coding. The key skills are not programming syntax but rather: defining requirements clearly, evaluating output critically, and thinking about what could go wrong.