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LangChain

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May 1, 2024 Updated July 11, 2025 13 minute read

LangChain is a platform that enables developers to build and deploy applications powered by large language models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. This makes them ideal for a wide range of applications, such as chatbots, language translation, and content generation.

Why Learn LangChain?

There are many reasons why you might want to learn LangChain. Here are a few:

  • Curiosity: LLMs are a fascinating new technology, and LangChain makes it easy to get started with them.
  • Academic requirements: LangChain can be used for a variety of academic projects, such as natural language processing (NLP) and machine learning (ML).
  • Career development: LLMs are becoming increasingly popular in the tech industry, and learning LangChain can give you a competitive edge in the job market.

How to Learn LangChain

There are many different ways to learn LangChain. Here are a few of the most popular:

  • Online courses: There are a number of online courses that can teach you LangChain. These courses typically cover the basics of LangChain, as well as how to use it to build different types of applications.
  • Documentation: LangChain has a comprehensive documentation website that can help you learn how to use the platform.
  • Community: There is a large and active LangChain community online. You can find help and support from other LangChain users on the LangChain Discord server and the LangChain GitHub repository.

Path to LangChain

Take the first step.
We've curated 24 courses to help you on your path to LangChain. Use these to develop your skills, build background knowledge, and put what you learn to practice.
Sorted from most relevant to least relevant:

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Reading list

We've selected three books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in LangChain.
Explores the linguistic properties of LLMs, including their ability to understand and generate text, translate languages, and answer questions. It also discusses the ethical implications of using LLMs.
Provides a Russian-language overview of LLMs, including their history, architecture, and applications. It is written in a clear and concise style, making it accessible to readers with a variety of backgrounds.
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