Quest 5 - Retrieval Augmented Generation using Atlas Vector Search
$2,550
$5
778
Learning Outcomes
By the end of this quest, you will be able to:
- Describe what RAG is
- Implement Atlas Vector Search in a RAG architecture
- Utilize open source embedding models and Large Language Models (LLMs)
Quest Details
Introduction
Welcome to the final quest of our campaign, where we will explore the integration of Atlas Vector Search with Retrieval-Augmented Generation (RAG). RAG is a cutting-edge approach in data science that combines the retrieval of relevant information with generative models to enhance the quality and relevance of the output.
In this quest, you'll witness firsthand the significant impact Atlas Vector Search can have on a typical data science workflow, particularly in tasks like RAG. We'll delve into how this powerful tool can streamline processes, enhance accuracy, and revolutionize the way we handle complex data science tasks.
For technical help on the StackUp platform & quest-related questions, join our Discord, head to the mongodb-helpdesk channel and look for the correct thread to ask your question.
Deliverables
This quest has 1 deliverable.
- A screenshot of your Python notebook after running the last code cell
This quest is part of a campaign so do check out other quests!
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