Project

Introduction to LangChain

Hard

82 completions

~ 10 hours

4.3

In this project, you will learn how to effectively interact with LLMs and design prompts to guide their behavior. You'll also discover how to integrate external data and utilize tools to expand LLM capabilities, allowing them to perform more complex tasks. Finally, you will learn how to build composable workflows using runnables, enabling you to create modular LLM applications.

Provided by

JetBrains Academy

About

The rise of large language models is changing how we process data and develop software. LangChain provides the essential tools to bridge the gap, making it easy to incorporate LLMs into your applications. This project offers a practical, hands-on introduction to LangChain. You'll learn the core concepts and techniques for developing intelligent, LLM-driven applications by building a system that answers questions about planets.

Graduate project

This project covers the core topics of the Introduction to AI Engineering with Python course, making it sufficiently challenging to be a proud addition to your portfolio.

At least one graduate project is required to complete the course.

What you'll learn

Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:

Set up your development environment and install necessary libraries to start building LLM applications.

Connect to LLM APIs and design effective prompts to guide LLM behavior and get desired outputs.

Integrate external data, create embeddings, and use vector search to enhance LLM applications.

Equip LLMs with tools to expand their capabilities, enabling them to interact with external systems and perform complex tasks.

Combine different LLM components using runnables to build complete applications and execute them effectively.

Reviews

Frank Vreys

2 months ago

4.0

Very interesting project. But difficult to find back useful information regarding Langchain: i.e. How to set-up .. , Examples,..

Sebastian Müller

3 months ago

5.0

he project brings together the key LangChain concepts nicely: prompt templates, tool binding, vector search, and runnable composition. It helped me better understand how LangChain applications can be structured in a modular and reusable way.

Roi Shamayev

3 months ago

4.0

project is decent about the basics. could use some more tasks to better retain the knowledge

4.3

Learners who completed this project within the Introduction to AI Engineering with Python course rated it as follows:

Usefulness

4.6

Fun

4.3

Clarity

4.0