Intelligent Data Validation - the Easy Way

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 to process 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. Digital voodoo to validate email address structure, format, and content.

The LLM powered way?

Just prompt: "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 the most nuanced data rules.

This method is highly effective, especially when combined with non-LLM validators. Have the LLM handle the semantic checks while a deterministic validator checks the LLM's structured output.

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

Cheers, Craig