A Practical Introduction to Vector Databases

Step into the backbone of modern information retrieval. Vector databases are redefining how systems understand and process data. In this course, we’ll explore embeddings, learn to compare traditional and vector-based methods, and apply optimization techniques for efficient querying. Through indexing, quantization, advanced re-ranking, and agentic RAG architectures, we’ll gain the knowledge to build retrieval pipelines ready for today’s most demanding applications.

What You’ll Learn

• Understand how vector databases differ from traditional databases and where they excel
• Apply embeddings and tokenization techniques to represent and search complex data
• Optimize performance using indexing, quantization, and approximate nearest neighbor methods
• Design advanced retrieval pipelines with hybrid search, re-ranking, and agentic RAG architectures