The Vibe Coder's Quality Field Guide
Stop Shipping Broken AI-Generated Code
A practical reference for developers who use AI to build — and want what they ship to actually work.
Why This Guide Exists
You've felt it. You describe what you want, the AI produces something that looks right, you run it, and it breaks. Or worse — it works in demo, breaks in production. Or it works today and breaks when you add the next feature.
This isn't a problem with AI. It's a problem with how most people use AI to build software.
This guide is the playbook for doing it right.
Part 1: Before You Write a Single Prompt
1.1 Define the Contract First
The #1 mistake: letting the AI design the architecture while you're describing a feature.
Before any code, write down:
- What goes in (inputs, formats, sources)
- What comes out (outputs, formats, destinations)
- What can fail (error cases, edge cases, limits)
- What success looks like (specific, measurable)
If you can't write this in plain English in 5 minutes, you're not ready to build it yet.
1.2 Scope Before You Build
Explicitly scope what you're NOT building:
- "No authentication in this version"
- "No database persistence — just in-memory"
- "No error handling beyond console.log for now"
This isn't laziness. It's discipline.
1.3 Choose the Right Model for the Job
| Task | Use |
|---|---|
| Architecture planning | Larger models (Opus, GPT-4) |
| Code generation | Sonnet, GPT-4o, Gemini Flash |
| Simple boilerplate | Haiku, smaller fast models |
| Debugging / explanation | Any strong model |
| Test writing | Strong + specialized |
Part 2: Prompting for Quality Code
2.1 The Quality Prompt Template
Bad prompt: "Write a function that processes payments"
Better prompt:
The difference: explicit types, explicit error cases, explicit boundaries.
2.2 One Function, One Responsibility
Never ask for more than one meaningful thing per prompt. Write 4 prompts for 4 parts. Review each output before moving to the next.
2.3 Ask for the Test Before the Code
"Write 5 test cases for a function that validates email addresses. Include normal cases, edge cases, and failure cases. Don't write the function yet."
Once the test cases are right, ask it to write the function that passes them.
2.4 The "What Could Go Wrong" Prompt
After any non-trivial code generation, ask:
"What are 5 ways this code could fail in production that aren't covered by the current error handling?"
The AI is often better at finding its own holes than you are.
Part 3: Architecture Patterns That Don't Collapse
3.1 Start With the Data Model
Every new conversation: paste this in as context. The AI will use it.
3.2 Boundaries Over Layers
3.3 Configuration at the Edges
Any hardcoded URL, port, timeout, threshold, or credential is a bug waiting to happen.
3.4 The "What If It's Down" Pattern
AI almost never handles unavailable dependencies. Add it yourself.
Part 4: Testing Without Losing Your Mind
4.1 The 3 Tests Every Function Needs
- Happy path: correct inputs → correct output
- Bad input: wrong types, missing fields → doesn't crash, returns useful error
- Edge case: empty list, zero, None, very long string, Unicode
If a function can't pass these three, it's not ready.
4.2 The "Break It" Prompt
After working code and tests:
"Without changing the tests, modify the function in a way that would make it fail in a real-world scenario but still pass all current tests."
If the AI can do this easily, your tests are bad.
4.3 Integration Test Before Polish
When this passes end-to-end, the core works. Now clean up.
Part 5: The Review Process That Actually Catches Problems
5.1 The 5-Minute Pre-Ship Checklist
Security
- [ ] No credentials or tokens in code
- [ ] User inputs validated before use
- [ ] SQL queries use parameterized values
Reliability
- [ ] Every external call has error handling
- [ ] Every async operation has a timeout
- [ ] No infinite loops without exit conditions
Correctness
- [ ] Integer math for money (not float)
- [ ] Timezones are explicit
- [ ] Null/None cases handled
5.2 Red Flags in AI-Generated Code
| Pattern | Problem |
|---|---|
| Silently swallowing errors | |
| Hardcoded waits | |
| without | KeyError waiting to happen |
| , | Security vulnerability |
Part 6: When AI Code Breaks in Production
6.1 The Debugging Protocol
Step 1: Reproduce in isolation — strip to 20 lines
Step 2: State actual vs. expected. Write it down.
Step 3: Ask the AI to explain, not fix:
"Walk me through exactly what this function does with this input. Don't suggest fixes yet."
Step 4: Ask for the fix with constraints.
6.2 When to Rewrite vs. Patch
Patch when: logic is right, fix is contained, you understand the break
Rewrite when: you don't understand the code, patching touches 5+ places, data model is wrong
Quick Reference Card
Before You Prompt
- Define inputs, outputs, error cases in plain English
- Scope what you're NOT building
- Establish data models and naming conventions
While Prompting
- One task per prompt
- Specify types explicitly
- Ask for tests before implementation
After Generating
- Ask "what could go wrong?"
- Run the checklist before shipping
- Integration test before polishing
When It Breaks
- Reproduce in isolation
- State actual vs. expected precisely
- Ask for explanation before fix
- Rewrite if you've patched 3+ times
If something in your project is broken and you want hands-on help, check out the Vibe Code Rescue service — code audit and fix starting at 9.
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