Data Validation: Engineering for Vibe Coders
Vibe coders often assume that the data entering their prototype is perfect. In reality, user input, APIs, and AI outputs can be incomplete, malformed, or unexpected. Without data validation, your prototype may break, produce wrong results, or behave unpredictably.
Data validation is about checking that incoming data meets expectations before it is used. Planning validation early helps you catch errors, improve reliability, and make your prototype more resilient.
1. Why data validation matters
Bad data is one of the most common causes of prototype failure. Even simple input errors can cascade into bigger problems, producing exceptions, incorrect outputs, or security risks.
Data validation helps you:
- Catch invalid or missing inputs
- Ensure API responses conform to expected formats
- Prevent errors before processing or storing data
- Improve user experience with meaningful feedback
🟢 Pre-prototype habit:
List all the sources of input your prototype will have: users, APIs, databases, AI models. Identify what could go wrong with each source before coding.
2. Types of validation
There are multiple layers of validation to consider:
- Type validation: Ensure data is the correct type (number, string, boolean, etc.)
- Range and length checks: Confirm numeric values are within limits and text isn’t too long or empty
- Format checks: Validate structure (emails, phone numbers, JSON objects)
- Required vs optional fields: Determine which data must always be present
- Business rules: Enforce domain-specific constraints (e.g., end date after start date)
🟢 Pre-prototype habit:
Sketch a simple table for each input field with type, required status, allowed range, and format. This makes your validation plan clear before you start coding.
3. Validation for AI and API outputs
AI models and external APIs can produce unexpected results. Validation is critical to avoid passing incorrect data downstream:
- Check that model outputs match expected structure
- Validate critical fields from APIs (e.g., IDs, timestamps, URLs)
- Handle empty, null, or malformed responses gracefully
- Plan fallback values or error handling for invalid outputs
🟢 Pre-prototype habit:
List key outputs from each AI model or API call and define simple checks that confirm the data is usable before further processing.
4. Lightweight validation strategies for prototypes
You don’t need complex libraries for early prototypes. Simple approaches work well:
- Use built-in type checks and conditional statements
- Validate required fields before saving or sending data
- Apply minimal regex or pattern matching for structured data
- Return meaningful messages to users instead of failing silently
🟢 Pre-prototype habit:
Decide which validation checks are essential for your prototype versus what can be added later. Start with the basics to catch critical errors early.
5. Quick pre-prototype checklist
| Checklist Item | Why It Matters |
|---|---|
| Identify all input and output sources | Determines where validation is needed |
| Define types, ranges, and formats for each field | Prevents unexpected values |
| Plan validation for AI and API responses | Avoids broken workflows |
| Decide lightweight validation approach | Ensures prototype stability without slowing development |
| Determine fallback behaviors | Keeps your prototype running even with invalid data |
Closing note
Data validation is not optional, even for prototypes. By planning how your AI prototype checks inputs and outputs, you prevent errors, improve reliability, and create a better user experience.
🟢 Pre-prototype habit:
Sketch all sources of input and key outputs, define simple validation rules, and plan fallback behaviors before writing any code. Early preparation keeps prototypes predictable, stable, and easier to scale.
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