Belgrade MUG: Building RAG Applications using MongoDB (Skill Badge Workshop)

Join us for a hands-on workshop where you’ll learn how to build Retrieval Augmented Generation (RAG) applications using MongoDB Atlas — and earn a Skill Badge to showcase your achievement!

LLMs are great at answering generic questions, but how do you get them to answer questions about your organization’s data? In this workshop, you will learn the foundational concepts of Retrieval Augmented Generation (RAG), including chunking, embedding, and semantic search. You will also gain hands-on experience by building an end-to-end, RAG-based chatbot using MongoDB Atlas and open-source LLMs. So, be sure to bring a laptop to get the most out of this session.

No local setup is required — the entire workshop environment is cloud-based, so you can dive right into building without worrying about configuration. Just bring your laptop and you're ready to go!

You will be provided with all the resources required to successfully execute the hands-on portions of the workshop, including a GitHub repository of Jupyter Notebook templates with pseudocode. During the workshop, you will replace the pseudocode in the templates with your code.

At the end of the workshop, you will also be able to check your learning and earn a skill badge to share with your network!

Where?

Event Type: In-Person
Location: Startit Centar (Savska 5, Beograd)

Agenda

|18:00 PM - 18:15 PM| - Gathering, networking, food
|18:15 PM - 20:15 PM| - RAG Workshop
|20:15 PM - 20:30 PM| - Skill Badge Test
|20:30 PM - 21:00 PM| - Networking, food, swags ...

Goals and Objectives

• Learn the foundations of Retrieval Augmented Generation (RAG), such as chunking, embedding, and semantic search.
• Perform semantic search queries against a MongoDB Atlas collection.
• Build a RAG system using MongoDB Atlas and open-source LLMs.
• Add memory to the RAG application.
• Earn MongoDB Skill Certificate

Practical applications of this knowledge

• Use MongoDB as a unified operational and vector store, as well as a memory provider for RAG applications
• Build production-grade RAG applications using MongoDB
• Improve existing RAG applications using techniques learnt