RAG to Riches: Crafting an Educational Quest with Wilco and MongoDB

RAG to Riches: Crafting an Educational Quest with Wilco and MongoDB
Written by
Wilco team
April 2, 2024

In the dynamic environment of software development, mastering the latest technologies and methodologies is crucial. AI stands out as a beacon of innovation. In collaboration with MongoDB, we've designed a quest to demystify the basics of Retrieval-Augmented Generation (RAG) using MongoDB. Drawing inspiration from a MongoDB blog post, this quest is crafted to offer a practical learning experience on implementing RAG for an AI chatbot.

The Genesis of the Idea

Our journey began with the ambition to unravel the complexities of AI's latest advancements. The chosen MongoDB blog post laid the groundwork, showcasing RAG as a revolutionary blend of elite database management and the pioneering capabilities of AI chatbots. Our objective was clear: simulate a real-world scenario where developers can navigate the nuances of RAG, thereby elevating their skills significantly.

A Quest About AI - Planned and Built Using AI

The quest's planning phase was revolutionized by our WilcoAI quest builder. A link to the MongoDB blog post was all that was needed for WilcoAI to generate a comprehensive quest skeleton. The focus then shifted to refining the quest, emphasizing the most impactful topics and ensuring valuable takeaways for the user.


Leveraging Wilco’s Platform for Quest Creation

The intuitive SDK and versatile quest creation tools provided by Wilco transformed the initial quest skeleton into a fully realized educational experience. By integrating MongoDB's technical prowess and the capabilities of OpenAI models into Wilco’s framework, we crafted a quest that is both informative and engaging.

A Glimpse into the Technical Core

Retrieval-Augmented Generation (RAG) is a booming technique that revolutionizes how AI systems produce responses by combining the power of retrieval from databases with the generative capabilities of models like those developed by OpenAI. This technique allows AI to pull in relevant information from a database as context, making its responses more accurate, informative, and tailored to the specific query at hand, all due to this contextual understanding..

In this quest, we leveraged OpenAI's embedding model to transform a sample database into a series of vectors. Vectors are essentially numerical representations that allow AI to understand and process text data in a mathematically efficient manner. When users pose a question, the quest mimics the real-world application of RAG by embedding the user's query into the same vector space. It then searches this space for the most relevant document vectors, effectively selecting pieces of the database that are most likely to contain the information needed to answer the query. This document—or a set of documents—serves as the context for the generative model to produce a detailed, accurate response.

This hands-on experience not only demystifies the process of using RAG but also empowers developers with the knowledge and skills to implement similar systems in their projects, leveraging the combined strengths of retrieval and generation for enhanced AI interactions.

The Final Product

This quest delivers an engaging and comprehensive exploration into the fundamentals of deploying a Retrieval-Augmented Generation (RAG) system using MongoDB. It starts with an introduction to MongoDB Atlas, a fully managed database service, guiding learners through the principles of vector search. Participants then progress to construct a fully functional React application, hosted in the cloud, that powers an AI chatbot. This chatbot utilizes a movie database and employs RAG technology to enhance the accuracy and relevance of its responses to user queries. Thanks to the Wilco playground, this app is primed to run within minutes, allowing learners to quickly transition from theory to hands-on practice. 

The React App built in the quest

Reflections on Using Wilco

Developing this quest underscored Wilco's strengths in educational content development. The platform not only facilitated but also enriched the creative process, making it straightforward to convert complex technical concepts into captivating learning experiences. This endeavor is a testament to Wilco’s capacity to seamlessly blend learning with practical application, offering a unique sandbox for hands-on problem-solving.


The RAG quest, inspired by MongoDB's thorough blog post, signifies a leap forward in technical education. By fusing theoretical knowledge with practical application, we've crafted a resource that educates and inspires. We invite developers to embark on this quest, exploring the vast potential of learning new techniques using Wilco, pushing the boundaries of their capabilities.

Additional Resources

- For a deeper dive into RAG, explore MongoDB’s

- Register now to MongoDB's quest.