This post, however, shifts focus on how we can leverage Pydantic's validation mechanism to minimize hallucinations. We'll explain how validation works and explore how incorporating context into validators can enrich language model result.
The intention is by the end of this article, you'll see some examples of how we can use Pydantic to minimize hallucinations and gain more confidence in the model's output.
But before we do that, lets go over some validation basics.
In the last year, there's been a big leap in how we use advanced AI programs, especially in how we communicate with them to get specific tasks done. People are not just making chatbots; they're also using these AIs to sort information, improve their apps, and create synthetic data to train smaller task-specific models.
What is Prompt Engineering?
Prompt Engineering is a technique to direct large language models (LLMs) like ChatGPT. It doesn't change the AI itself but tweaks how we ask questions or give instructions. This method improves the AI's responses, making them more accurate and helpful. It's like finding the best way to ask something to get the answer you need. There's a detailed article about it here.
While some have resorted to threatening human life to generate structured data, we have found that Pydantic is even more effective.
In this post, we will discuss validating structured outputs from language models using Pydantic and OpenAI. We'll show you how to write reliable code. Additionally, we'll introduce a new library called instructor that simplifies this process and offers extra features to leverage validation to improve the quality of your outputs.
We're excited to announce the first alpha release of Pydantic V2!
This first Pydantic V2 alpha is no April Fool's joke — for a start we missed our April 1st target date .
After a year's work, we invite you to explore the improvements we've made and give us your feedback.
We look forward to hearing your thoughts and working together to improve the library.
For many of you, Pydantic is already a key part of your Python toolkit and needs no introduction —
we hope you'll find the improvements and additions in Pydantic V2 useful.
If you're new to Pydantic: Pydantic is an open-source Python library that provides powerful data parsing and validation —
including type coercion and useful error messages when typing issues arise — and settings management capabilities.
See the docs for examples of Pydantic at work.