Developing LLM Applications with LangChain
DataCamp
Last updated June 30, 2026
A hands-on course in building applications on top of large language models using LangChain, a framework that ties models, prompts, and tools together. Working in Python, you connect to both OpenAI and Hugging Face models, build chatbots with reusable prompt templates, and compare open-source against closed-source models in practice. From there it moves into retrieval — tokenization, vector databases, and retrieval-augmented generation — and then into chains, tools, and agents for more advanced behaviour, with attention to debugging and measuring performance. You finish able to assemble a working LLM application rather than just call a model. This is a coding course built on Python.
What you'll learn
- Connecting to OpenAI and Hugging Face models through LangChain
- Building chatbots with reusable prompt templates
- Comparing open-source and closed-source models in practice
- Tokenization, vector databases, and retrieval-augmented generation
- Chains, tools, and agents, with debugging and performance metrics
Frequently asked questions about Developing LLM Applications with LangChain
Who is Developing LLM Applications with LangChain for?
Developers with some LLM and Python experience who want to build structured applications with LangChain.
Is Developing LLM Applications with LangChain free?
No — Developing LLM Applications with LangChain is a paid course.
What are the prerequisites for Developing LLM Applications with LangChain?
Some prior LLM coding — comfort with embeddings and prompting via the OpenAI API.
Do you need to code for Developing LLM Applications with LangChain?
Yes — Developing LLM Applications with LangChain involves hands-on coding.
Does Developing LLM Applications with LangChain offer a certificate?
Yes. DataCamp Statement of Accomplishment on completion (requires DataCamp Premium).
Why we suggest this course
A practical framework course for developers ready to go past single API calls and build structured LLM applications. It covers the pieces a real application needs — prompt templates, retrieval, chains, tools, and agents — and includes debugging and performance, which application-building tutorials often skip. It builds on prior work with embeddings and prompting using the OpenAI API, so it suits a developer with some LLM coding behind them. You can sample the opening chapter before subscribing; the full course and its Statement of Accomplishment are part of DataCamp Premium.