Welcome to Pydantic for LLM workflows. In this course, you'll learn how you can use the Pydantic Python package to get an LLM to give you structured output. And by that, I mean output that's formatted exactly the way you want it to be. So of course, In general, LLMs produce free form text. And that can be great if you're doing something like summarizing an article or maybe coming up with an idea for a new recipe. But if you're building an LLM into a larger software system where you have multiple components and you want to be able to pass the data you're getting from an LLM to the next component in the system in a predictable way, that's when pydantic and structured output can be a big help. So for example, imagine you're building a customer support application where the user can submit a request. Maybe something like Hey, I forgot my password. or a complaint like, I'm not happy with the product I bought. You could pass that customer query to an LLM and have it generate a structured response that might look like this, where you have the user's information, like their name and email, the text of their request, a priority and a category. And then you might record some other things, like whether or not this request is a complaint. And maybe some tags or keywords about the request. Then your system could take this structured response and decide what to do next. Like if it's an urgent issue, you might create a support ticket and route it to a human support agent who can follow up and help. Or for this simple request of I forgot my password, you might pass the structured response to another LLM agent that can call a tool, like a function that can look up an FAQ response that could help the user. So maybe in this case that would be an FAQ response with a password reset link and instructions. So whether it's this step of having an LLM create a structured response that becomes a Support Ticket with this exact set of fields and content, or having an LLM provide the parameters you need. to call a tool and look up an FAQ response. You have very precise expectations for what each of those LLM responses needs to look like and what kind of data it needs to contain. So in this course, you'll learn different methods for using Pydantic to ensure that an LLM is giving you exactly the data that you need. Or in other words, you'll learn to validate the data that you're getting in a response from an LLM. And along the way, you'll also be learning data validation skills that can help you with handling a wide variety of data in really any kind of software system. And that might include things like human input to the system or external APIs and data sources, or really any data that you're wanting to pass from one component to the next in your system. It turns out that pydantic has actually been around since well before LLMs were a thing. And it's one of the most popular data validation frameworks. works out there. In fact, Pydantic sees over 300 million downloads per month, making it not just one of the most popular data validation frameworks, but really one of the most popular Python packages, period. And that's because data validation is at the core of really any software application. And so, in the lessons that follow, you'll be learning how you can use Pydantic to get structured output from an LLM. And you'll also be building data validation skills that you can use in any software application where you want to pass data from one component to the next. So, let's go on to the next video to get started.
Pydantic for LLM Workflows