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Good LLM Validation is Just Good Validation

What if your validation logic could learn and adapt like a human, but operate at the speed of software? This is the future of validation and it's already here.

Validation is the backbone of reliable software. But traditional methods are static, rule-based, and can't adapt to new challenges. This post looks at how to bring dynamic, machine learning-driven validation into your software stack using Python libraries like Pydantic and Instructor. We validate these outputs using a validation function which conforms to the structure seen below.

def validation_function(value):
    if condition(value):
        raise ValueError("Value is not valid")
    return mutation(value)