Intelligence is only as effective as the context you provide to the model. To build applications that solve complex problems, you need to move beyond simple text prompts and embrace retrieval-augmented generation (RAG) pipelines that leverage the full spectrum of your data.
This session explores how to integrate multimodal data types—including images and video—into your AI architecture to ground your models in reality. We demonstrate how to reduce hallucinations and improve accuracy by ensuring your LLM has access to a live, unified stream of truth rather than a static dataset.
Key Takeaways
Strategies for managing and querying diverse data types to provide richer context for your models.
Architectural patterns for building RAG pipelines that minimize hallucinations and improve response quality.
How to leverage MongoDB’s flexible schema to store and retrieve multimodal embeddings at scale.
Techniques for grounding model outputs in real-time data to maintain application reliability.
Watch this recorded session to see a hands-on breakdown of the workflows defining the current frontier of AI development. Our presenters walk through the technical hurdles of multimodal integration and provide the sample code you need to start building. Watch the full recording and see how a sophisticated data strategy can transform your application’s performance.
