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)