First Steps With LangChain (Overview)

Martin Breuss

First Steps With LangChain Martin Breuss 01:53

You’ve likely interacted with large language models (LLMs), like the ones behind OpenAI’s ChatGPT, and experienced their remarkable ability to answer questions, summarize documents, write code, and much more.

While LLMs are remarkable by themselves, with a little programming knowledge, you can leverage libraries like LangChain to create your own LLM-powered applications that can do just about anything.

In this video course, you’ll learn how to:

Use LangChain to build LLM-powered applications
Create reusable instructions with prompt templates
Create and extend LangChain chains
Debug what happens when a chain executes

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00:00 Welcome to this course where you’ll take your first steps with LangChain to interact with large language models. In this course, you learn how to use chat models with LangChain, how to scaffold and reuse prompts with prompt templates, which is a very powerful feature that makes your LLM applications a lot more maintainable.

00:21 You’ll learn how to build chains, which allow you to connect different steps and pipe outputs from one step into the next one. And this is kind of the heart of LangChain, so this is an important concept to properly grasp, and you’ll learn how you can inspect the inputs and outputs to chains using the built-in debug mode.

00:39 Now, if that sounds interesting to you, keep going with this course.

00:43 You should know a couple of things before you get started. You will need to have an installation of Python 3.10 or newer. I will work with 3.13 in this course.

00:52 You should have a basic understanding of object-oriented programming in Python, virtual environments, and how to work with environment variables. You should also have some experience interacting with LLMs because that will make the whole thing make a lot more sense.

01:06 And that’s all. I hope you’re ready. Let’s ask the chat model whether it’s ready.

01:15 That’s not very motivational. What’s all that data here? Well, I guess there is a piece of motivational output here. Absolutely. What would you like to get started with?

01:24 But it seems like the LLM is missing some context. Well, fortunately, you’re going to learn how to build chains like this motivational chain. And now if I’m going to ask it, if it’s ready to get started, I’ll get the output that I actually want.

01:39 Nicely formatted, “I’m ready, let’s do this!” So I hope you’re ready too. If you are, then move on to the next lesson where you’ll get set up so that you can start interacting with large language models using LangChain.

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