DataTalksClub/llm-zoomcamp

LLM Zoomcamp: Free Course on Building LLM Applications with RAG, Agents & Vector Search

Go from LLM basics to a production-ready AI assistant in 10 weeks

Learn Retrieval-Augmented Generation, vector search, embeddings, AI agents, function calling, evaluation, monitoring, hybrid search, reranking, and more - all in a free, open-source, hands-on course by DataTalks.Club.

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LLM Zoomcamp teaches you how to build practical, production-ready LLM applications step by step. Over 10 weeks you'll learn Retrieval-Augmented Generation, vector search, embeddings, AI agents, function calling, evaluation, monitoring, hybrid search, reranking, and more - all free, open-source, and hands-on.

This course is for people who learn by doing. After completing it, you'll have a working codebase and the hands-on experience to build your own LLM-powered applications.

• Software Engineers: Add LLMs, RAG, and modern search capabilities to real products
• Data Engineers: Understand how vector search, hybrid search, and retrieval pipelines fit into production systems
• ML Practitioners: Get a structured way to evaluate and monitor LLM-based applications

• Python: You can write code confidently
• Command Line: Comfortable with terminal
• Docker: Basic familiarity
• ML / LLMs: Not required
• Hardware: Any laptop or PC. No GPU needed
• Expenses: ~$1-5 in API credits

There are two ways to follow the course: live and self-paced.

Self-paced steps:

1. Follow the materials on GitHub
2. Ask questions and share progress in Slack
3. Do homeworks (self-checked) and build a project for your portfolio

• Build a RAG pipeline with keyword search
• Make it agentic with function calling

• Semantic search with embeddings
• minsearch, sqlitesearch, and PGVector

• AI orchestration with Kestra

• Build dlt pipelines to ingest and analyze LLM traces
• Filesystem and REST API sources, DuckDB, and marimo dashboards

• Measure retrieval and answer quality
• Offline and online evaluation

• Monitor user feedback and system health
• Live dashboards

• LangChain
• Hybrid search: combine vector and keyword search
• Rerank results for higher precision

• A complete project example: a fitness assistant built with LLMs

• Ship a complete end-to-end project of your choice from scratch

Recommended approach:

1. Watch the video for each module
2. Complete the homework to reinforce the concepts
3. Build your capstone project applying everything end-to-end

The capstone is your chance to apply everything end-to-end. You'll build a complete, working RAG application built and owned by you.

What you'll build:

• A searchable knowledge base. Choose a dataset, ingest, clean, and store it for retrieval
• A retrieval pipeline. Implement the full RAG flow: retrieve context, assemble prompts, call an LLM, return grounded answers
• An evaluation process. Measure how well your system retrieves and answers using search metrics or LLM-as-a-Judge
• A user-facing interface. A simple UI or API (Streamlit, FastAPI, or similar) so others can try your app
• Monitoring & feedback loops. Track queries, feedback, and performance over time

• Fitness & nutrition assistant
• Study companion for textbooks or course notes
• Medical FAQ assistant
• Codebase Q&A bot
• News summarization and retrieval tool

To earn your certificate:

1. Complete the final project. Build a real-world RAG application demonstrating all course concepts
2. Peer review 3 projects. Evaluate and provide written feedback on three fellow students' submissions
3. Meet the deadlines. Submit your project and reviews within the cohort schedule

Certificates are issued after all peer reviews are completed. Self-paced learners are not eligible for certification but can build portfolio projects freely.

See the certificate guide for how the certificate is issued and how to add it to LinkedIn.

• Alexey Grigorev - Founder of DataTalks.Club and creator of the Zoomcamp series. Principal data scientist.
• Will Russell - Developer Relations at Kestra.
• Timur Kamaliev - AI engineer building production LLM systems, RAG pipelines, and agentic applications.

"This course gave me hands-on experience in building LLM-powered applications, including prompt engineering, retrieval-augmented generation (RAG), pipeline orchestration, and vector search optimization."

— Alexander Daniel Rios, LLM Zoomcamp Graduate

"Not gonna lie - this course took longer than planned. By the end, I was running on fumes, forcing myself to push through the final modules. But I made it. What I loved: hands-on experience building real AI systems (not just theory!), deep dives into RAG, vector databases, evaluation, and monitoring, and the wealth of production-ready practices that matter in enterprise environments."

— Vasiliy Chernykh, LLM Zoomcamp Graduate

Read more testimonials from past graduates →

Join the #course-llm-zoomcamp channel on DataTalks.Club Slack for discussions, troubleshooting, and networking with fellow learners and the course team.

To keep discussions useful for everyone:

• Follow our posting guidelines when asking questions
• Review the community guidelines

We actively encourage sharing your progress online throughout the course. Post what you're building on LinkedIn, X, or a blog. It helps you get noticed and connect with others in the field. It also earns you bonus points toward your homework and project scores.

If you want extra support and structure while going through the course, AI Shipping Labs is an optional paid community focused on AI engineering. On top of the free course it adds office hours, accountability sprints, personalized onboarding, and member-only workshops. It is also a way to support the course.

Everything we do at DataTalks.Club is free and will stay free. AI Shipping Labs is only for people who want extra on top of the free content. See the AI Shipping Labs guide for what's included and how membership works.

A huge thanks to our sponsors for making this course possible!

A few common questions. For everything else, see the full LLM Zoomcamp FAQ.

Q: Is this course really free?
A: Yes. All videos, materials, and homework are free. You may spend $1-5 in OpenAI API credits if you run the code yourself.

Q: Do I need a GPU?
A: No. All exercises are designed to run on a standard laptop using cloud APIs.

Q: What does "live cohort" mean? Are there live classes?
A: No mandatory live classes. All lectures are pre-recorded. "Live" means deadlines, scored homework, a leaderboard, peer review, and certificate eligibility.

Q: Can I take it self-paced, and will I get a certificate?
A: Yes, you can start anytime. Certificates require completing the final project and peer reviews during a live cohort.

Found a bug in the course materials? Know how to improve an explanation or fix broken code? Contributions are welcome and appreciated.

1. Fork the repository
2. Make your fix or improvement
3. Open a pull request with a clear description

Every contribution helps future learners. Thank you 🙏

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