RAG and vector databases are the backbone of modern generative AI—but most product managers don’t understand how they actually work.
In this video, you’ll learn how embeddings, vector databases, and retrieval-augmented generation fit together to power smarter AI products. We’ll also break down how teams choose infrastructure based on latency, scale, and cost.
Inside this guide:
What embeddings are and why semantic meaning matters.
How vector databases make similarity search fast.
How RAG connects retrieval with generation for better answers.
The difference between keyword search and semantic search.
How infrastructure choices affect performance, scalability, and cost.
Why system design and query optimization can matter more than the algorithm itself.
If you’re building chatbots, knowledge assistants, search, or any AI product that uses proprietary data, this is the framework you need.
#AI #RAG #Embeddings #VectorDatabase #ProductManagement #MachineLearning #GenAI
