Your large language model is only as smart as the data you give it.
A large language model (LLM) is a powerful engine, but without the right fuel, it hallucinates or provides generic, outdated answers. Retrieval-augmented generation (RAG) is the bridge between a pre-trained model and your proprietary, real-time data.
In this session, we move from theory to execution, showing you how to build a high-performance RAG pipeline that anchors your AI in facts. You’ll learn how to orchestrate the flow of data from your MongoDB collection to the model prompt, ensuring every response is contextually rich, accurate, and secure.
- Optimize retrieval workflows to fetch the most relevant data chunks for your LLM prompts.
- Manage the "context window" effectively to balance accuracy with computational cost.
- Implement metadata filtering to narrow down vector searches and improve response precision.
- Build a feedback loop that ensures your RAG application remains performant as your data grows.
Stop settling for AI that guesses and start building AI that knows. Watch our experts solve specific RAG challenges and earn your MongoDB Skill Badge.
Continue learning:
- The Modern Data Architecture Mastery Series: Relational to Document Model
- The Modern Data Architecture Mastery Series: MongoDB Atlas Vector Search Fundamentals
- The Modern Data Architecture Mastery Series: Engineering Autonomous AI Agents
- The Modern Data Architecture Mastery Series: MongoDB Sharding Strategies
