Struggling with data validation in your code?

Let's talk about a game-changer in data validation: LLM powered validators.

Python Instructor's unique feature offers
software developers an accessible and adaptable method to define data
validations for non-deterministic LLM responses.

Simply put, you challenge the LLM to correct errors until it gets it right,
exhausts max retries, you get bored, or run out of API credit.

Take email verification...

The classic method: a hair-raising, difficult-to-debug regular expression to
validate email address structure, format, and content.

The LLM powered way? Just prompt it: "Validate that the email is in a correct
format and looks legitimate. Consider domain reputation and common typos."

LLM validators make it easy to auto-prompt LLMs to enforce data rules.

This method is highly effective, especially when combined with non-LLM
validators: the LLM handles the clever checks, while a deterministic check
validates the LLM's structured output.

Have you tried using LLM validators in your AI projects? What challenges or
benefits did you face?