Getting Started

For cross-course onboarding, read Joining a Cohort first. It covers registration, platform account setup, calendar, and newsletter updates.

This page covers the LLM Zoomcamp specifics.

Star the GitHub repository

github.com/DataTalksClub/llm-zoomcamp

Star the repo so you can find it later. All course materials are there, and each module has its own folder. The course team adds cohort-specific homework, deadlines, and launch-stream links under cohorts/ when published.

When many new participants star the repo around launch time, GitHub may surface it on Trending. That pulls more people into the cohort.

Join the LLM Zoomcamp Slack channel

After joining the DataTalks.Club Slack workspace, find the channel:

#course-llm-zoomcamp

This is your primary support and Q+A channel for the course. Use threads when replying. Don’t tag instructors directly - other participants often answer first, and tagging discourages that.

t.me/llm_zoomcamp is the announcement-only channel. Telegram is the most reliable place to catch important updates because Slack gets noisy. Telegram announcements are auto-reposted to Slack, so you do not strictly need it.

Subscribe to YouTube

The lectures are pre-recorded and live in two playlists on the DataTalks.Club YouTube channel:

  • LLM Zoomcamp main playlist: the canonical pre-recorded module videos.
  • LLM Zoomcamp 2026: cohort-specific recordings (launch stream, office hours).

For all the playlist links, see Resources.

Bookmark the FAQ

The LLM Zoomcamp FAQ collects answers to module-specific and technical questions from previous cohorts. Check it before posting in Slack.

There is also a community-built bot that answers questions using the FAQ and Slack history. The link is saved in the course Slack channel.

Pick your LLM provider

The course uses OpenAI in its examples. You are free to use any provider for your own work and project.

Practical options:

  • OpenAI: the default. A few dollars of credit covers the whole course.
  • Groq: free tier is generous enough to complete the course end to end.
  • Ollama or similar: for fully local, no-API workflows on a normal laptop.

For tradeoffs and the recommended setup, see Environment Setup.

Set up your environment

The course uses Python with Docker for the supporting services. Most lessons use notebooks.

You will install:

  • Python (3.10 or newer).
  • Docker.
  • Jupyter (covered in module 1).
  • An LLM provider account (OpenAI key, Groq key, or both).

For the choices you need to make, see Environment Setup.

Start Module 1

Begin with Module 1: RAG and Vector Search. Module 1 covers LLMs, RAG, and a FAQ assistant with keyword search and vector search. For the full module list, see Curriculum.

Next pages

Use these pages next: