What you'll learn

Implementing vector search for AI applications requires choosing between extending existing databases or adopting specialized vector databases, with challenges including understanding how general-purpose databases support vector operations, designing hybrid schemas that combine structured metadata with embeddings, evaluating performance tradeoffs, managing embedding ingestion pipelines, and building production-ready semantic search systems—often resulting in suboptimal architecture decisions, inefficient queries, and complexity in integrating vector capabilities into existing infrastructure. In this course, Vector Search for General Databases, you'll gain the ability to implement and optimize vector search solutions across different database platforms for RAG and semantic search applications. First, you'll learn about the basics of Retrieval Augmented Generation, which is used to pass custom data to the GenAI Model. Then, you'll learn how to implement RAG using different vector databases. When you're finished with this course, you'll have the skills and knowledge of vector database architecture and implementation needed to make informed decisions about vector search solutions and build production-ready AI applications that balance performance, cost, and operational complexity.