The transition from a simple AI prototype to a production-ready application requires a data strategy that handles the nuance of the real world. Most developers start with rigid keyword matching, but truly intelligent applications require a platform that can manage vector embeddings and scale as your workflows grow.
This session focuses on the architectural shifts necessary to ground your large language models (LLM) in actual context, moving beyond the basics to show you how unstructured data becomes a competitive advantage. You will see exactly how to implement semantic search within MongoDB Atlas to create user experiences that feel more natural and intuitive.
Key Takeaways
How to transition from traditional, rigid search patterns to vector-based semantic retrieval.
The fundamentals of how embeddings represent unstructured data within a unified platform.
Practical steps to implement vector search in MongoDB Atlas to improve query relevance.
Methods for scaling your search infrastructure horizontally to support growing AI workloads.
We’ve designed this session to be an engaging and educational look at the building blocks of modern AI. You’ll have the chance to see the technical resources and sample code used by our expert presenters to bridge the gap between theory and production. Even if you couldn’t make the live event, you can watch the recording now to ensure your data strategy is ready for the next evolution of software.
