RAG with MongoDB
Discover how to build Retrieval-Augmented Generation (RAG) applications with MongoDB. Learn to integrate vector search, optimize retrieval workflows, and enhance LLM-powered apps.
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Upon completion of the RAG with MongoDB skill check, you will earn a Credly Badge that you are able to share with your network. |
Learning Objectives
Identify the Architecture of a RAG Application
Describe the components of a RAG application and understand how they interact with each other to make the most of your application.
Describe Chunking Strategies
Results from your RAG application are only as good as the data you give it, learn how to chunk data and prepare it for use in a RAG system.
Create a RAG Application
Build a RAG application with LangChain and MongoDB that can answer questions based on your own data.
Note: The AI models used this learning content, including LLMs and embedding models, are constantly evolving and may have been updated since its creation.
Who is this Course Good for?
RAG with MongoDB is designed for application developers who want to build more intelligent, reliable GenAI experiences by grounding large language models in their own data. If you are already comfortable writing code, working with APIs, and using MongoDB or other modern databases, this skill badge will help you advance from basic chatbot experiments to production-ready Retrieval Augmented Generation (RAG) systems. It is a strong fit for backend and full-stack developers who are exploring vector search, integrating LLMs into existing services, or modernizing legacy applications with AI-powered features.
You do not need prior experience with RAG or vector search to succeed in this course. The material starts from first principles, then quickly becomes practical and hands-on. You will benefit most if you understand fundamental database concepts and are curious about how GenAI applications actually retrieve and use context behind the scenes. Developers who are evaluating different RAG architectures, comparing approaches like ChatGPT-style models versus domain-specific assistants, or trying to reduce hallucinations in their current GenAI projects will find this course especially valuable.
What to Expect in this Course
In this skill, you will learn how to use Atlas Vector Search to implement a complete RAG pipeline that powers a question-answering application backed by MongoDB. You will see how RAG combines retrieval and generation so large language models can respond using your data instead of generic internet text. Using a concrete example based on “The Little Book of MongoDB,” you will build an application that answers questions using book content stored and queried through MongoDB.
You will begin by unpacking what RAG is and how it differs from a general-purpose model such as ChatGPT. You will explore how RAG systems retrieve relevant documents, pass them to an LLM as context, and return grounded responses that reflect your documentation, scripts, or other private content. Along the way, you will learn why vector search matters for GenAI: how embeddings make it possible to find semantically similar content, and how Atlas Vector Search indexes and queries those vectors efficiently.
From there, the course walks through the end-to-end workflow for building a RAG application with MongoDB. You will identify and sanitize your data sources, split content into chunks, generate embeddings, and ingest everything into MongoDB collections. You will then implement the retrieval layer using Atlas Vector Search and wire it to a generation component that calls an LLM with both the user’s question and the retrieved context. Labs throughout the course provide realistic scenarios so you can practice configuring vector search, tuning your prompts, and validating that the RAG system is returning accurate, domain-specific answers for your GenAI use cases.
Summary of the Course
- Understand the core concepts of Retrieval Augmented Generation (RAG) and how it differs from generic LLM usage.
- Explain how embeddings and vector search work together to power semantic retrieval in GenAI applications.
- Prepare and transform real-world data sources, including cleaning, chunking, and generating embeddings for storage in MongoDB.
- Configure Atlas Vector Search indexes and queries to retrieve the most relevant context for a given user question.
- Build a question-answering application that uses content from “The Little Book of MongoDB” as its knowledge base.
- Implement a retrieval component that feeds relevant MongoDB documents to an LLM as part of a RAG pipeline.
- Implement a generation component that produces grounded responses using both the user’s query and retrieved context.
- Practice diagnosing and improving RAG behavior through hands-on labs that simulate realistic developer scenarios.
- Apply the patterns from the course to design your own RAG and vector search workflows for future GenAI applications with MongoDB.
Parker Faucher| Senior Software Engineer
Parker is a Senior Software Engineer on the Education team at MongoDB. Prior to joining MongoDB, he helped maintain a world class developer bootcamp that was offered in multiple universities. He is a self taught developer who loves being able to give back to the community that has helped him so much.
Emily Pope | Senior Curriculum Designer
Emily Pope is a Senior Curriculum Designer at MongoDB. She loves learning and loves making it easy for others to learn how and when to use deeply technical products. Recently, she's been creating AI and vector search content for MongoDB University. Before that, she's created learning experiences on databases, computer science, full stack development, and even clinical trial design and analysis. Emily holds an Ed.M. in International Education Policy from Harvard Graduate School of Education and began her career as an English teacher in Turkiye with the Fulbright program.
Joel Lord | Lead, Curriculum Eng/Technologist
Joel Lord is a curriculum engineer at MongoDB who is committed to empowering developers through education and active community involvement. With more than twenty years of experience in software development, developer advocacy, and technical education, he combines extensive expertise with a dedication to making complex topics more understandable.
Holding a Bachelor of Science in computational astrophysics from Université Laval, Joel started his career in web development before he focused on assisting others in their learning journeys. At MongoDB, he develops educational materials designed to equip developers with the skills to build improved applications, drawing on his wide-ranging experience as a speaker at global conferences.
When he is not working, Joel enjoys stargazing in remote camping areas, experimenting with inventive brewing methods in his garage, and offering emotional support to his two cats, who often appear as guests during his Zoom meetings.
Harshad Dhavale | Staff Technical Services Engineer
Harshad Dhavale is a Staff Technical Services Engineer, who has been with MongoDB for over six years. He is a subject matter expert in Atlas Search and Atlas Vector Search, and has made significant contributions in these domains over his tenure. In addition to enablement, he has played a key role in numerous pivotal Atlas Search and Atlas Vector Search initiatives and projects, which have been instrumental in defining the product's trajectory. He is a trusted specialist on all things Search, and enjoys diving deep into complex Search topics.
Henry Weller | Product Manager
Henry Weller is the dedicated Product Manager for Atlas Vector Search, focusing on the query features and scalability of the service, as well as developing best practices for users. He helped launch Atlas Vector Search from Public Preview into GA in 2023, and continues to lead delivery of core features for the service. Henry joined MongoDB in 2022, and was previously a data engineer and backend robotics software engineer.
John McCambridge | Curriculum Engineer
John is a Curriculum Engineer on the University team at MongoDB. Before his work as a Curriculum Engineer, he was an instructor and teaching assistant for coding boot camps at UT (Austin), and UCLA. Additionally, he worked as a QA engineer for a startup called Coder and spent five years at Apple Inc. John is a passionate software engineer and educator who enjoys taking complex topics and making them digestible for the community.
Sarah Evans | Senior Curriculum Engineer
Sarah is a Senior Curriculum Engineer on the Curriculum team at MongoDB. Prior to MongoDB, she taught and developed curricula for developer bootcamps. Sarah has a MAT degree from Columbia University Teachers College and studied Software Engineering at Flatiron School in Chicago, IL.
Manuel Fontan Garcia | Senior Technologist, Education
Manuel is a Senior Technologist on the Curriculum team at MongoDB. Previously he was a Senior Technical Services Engineer in the Core team at MongoDB. In between Manuel worked as a database reliability engineer at Slack for a little over 2 years and then for Cognite until he re-joined MongoDB. With over 15 years experience in software development and distributed systems, he is naturally curious and holds a Telecommunications Engineering MSc from Vigo University (Spain) and a Free and Open Source Software MSc from Rey Juan Carlos University (Spain).
Daniel Curran | Senior Software Engineer
Daniel is a Senior Software Engineer at MongoDB. Before joining MongoDB, he worked as an Instructional Designer and Content Developer specialising in technical content for a host of international clients. Daniel's goal is to remove obstacles so learners can feel confident on their journey to become masters of MongoDB.
Katie Redmiles | Instructional Designer
Katie is a Instructional Designer at MongoDB. Before joining the Curriculum team, Katie worked on the University Enablement team developing Learning Bytes and instructional materials for the MongoDB for Academia program. Katie also worked within the Digital Learning Division at the Foreign Service Institute where she honed her skills at developing online learning for a global audience. Katie is passionate about making education accessible and engaging for everyone.
