Taking RAG to Production with the MongoDB Documentation AI Chatbot

Taking RAG to Production with the MongoDB Documentation AI Chatbot

Explore how MongoDB enhances developer support with its innovative AI chatbot, leveraging Retrieval Augmented Generation (RAG) technology. This article delves into the technical journey of creating an AI-driven documentation tool, discussing the RAG architecture, challenges, and solutions in implementing MongoDB Atlas for a more intuitive and efficient developer experience. Discover the future of RAG applications and MongoDB’s pivotal role in this cutting-edge field.

Read more on Developer Center

Author: Ben Perlmutter

2 Likes

Amazing article.

I’m wondering, how do you handle updating embeddings? Do you replace them entirely when content changes, and if so, how do you handle the cost impact of doing so?

In addition, what are some criteria(s) you were able to identify that would cause an update to be done?(e.g: if a typo was made, maybe it’s not worth updating)

Thanks for sharing this with us.

Hi,

I’m curious about how you handle updating embeddings. Do you replace them entirely when content changes? If so, how do you manage the cost impact of doing so?

Additionally, what criteria do you use to determine when an update is necessary? For example, would a minor typo warrant an update, or are there more significant changes that trigger this process?

Thanks for sharing your insights with us!


Does this look good to you?