<!--
title: Categorize error types
domain: app-frameworks
topic: Error Handling
language: Markdown
source: pydantic/pydantic
updated: 2025-02-24
url: https://awesomereviewers.com/reviewers/pydantic-categorize-error-types/
-->

Distinguish between different categories of errors and handle each appropriately. Specifically:

1. **Validation errors** occur during normal operation when input data doesn't match expectations. These should be caught and handled gracefully.
2. **Usage errors** indicate developer mistakes in using your API and generally shouldn't be caught.

Document these distinctions clearly for API users and provide correct alternatives for common error scenarios.

```python
# BAD: Catching usage errors that indicate developer mistakes
try:
    # Code that might produce both validation and usage errors
    result = model(invalid_input)
except Exception:  # Too broad!
    log_error("An error occurred")
    
# GOOD: Only catch expected validation errors
try:
    result = model(user_input)
except ValidationError as e:
    # Handle validation errors gracefully
    log_error(f"Validation failed: {e}")
    show_user_friendly_message()
    
# Usage errors (like PydanticUserError) aren't caught,
# allowing them to crash during development when they can be fixed

# In documentation, clearly explain error categories:
"""
## Error Types

### Validation Errors
Errors that happen during data validation when user input doesn't match schema.
These can be caught and handled at runtime.

### Usage Errors 
Errors that happen when using the API incorrectly.
These indicate developer mistakes and shouldn't be caught.
"""
```