LangChain
Pipelines for LLM applications: prompts, tools, memory.
LangChain is taught here in 3 lessons, running from Prompt templates and chains through to Retrieval-augmented generation. Each lesson takes one topic, shows the working code, and links onward to the next, so the course can be read straight through in order.
| Track | AI & Intelligent Development |
| Lessons | 3 |
| Level | Beginner |
| Reading time | about 39 minutes |
| Prerequisites | Helpful, but not required: Natural Language Processing |
Lessons
- Prompt templates and chainsBuild prompts that stay clean at scale, then compose them into chains with the pipe operator instead of nested function calls.
- Tools and agentsExpose Python functions to a model, let it choose between them, and keep the loop bounded and safe.
- Retrieval-augmented generationChunk and index documents, retrieve the right passages, and answer with citations while allowing an honest not-found reply.
More in AI & Intelligent Development
AI Basics AI Agents Math for AI Machine Learning scikit-learn TensorFlow PyTorch Natural Language Processing Ollama OpenCV Codex Claude Code OpenCode Vibe Coding Selenium Playwright
FAQ
How long does the LangChain course take?
It has 3 lessons, about 39 minutes of reading. Expect roughly twice that if you type out and run every example.
Do I need prior experience for LangChain?
Not strictly. It helps to have read Natural Language Processing first, because some lessons build on it, but every lesson explains its own assumptions.
What should I read after LangChain?
Continue with Ollama (3 lessons), the next course in AI & Intelligent Development.
Last refreshed 2026-09-18.