This intermediate-level course is designed for database professionals, ML engineers, and AI practitioners who need to build and maintain high-performance LLM systems. In the world of large-scale AI, slow queries and inefficient search can bring a system to its knees. This course provides the critical skills to prevent that, focusing on practical database and vector search optimization techniques.

You will learn to master parameterized SQL queries to ensure secure, efficient data retrieval and diagnose performance bottlenecks. You will then dive into the core of modern AI retrieval systems, learning to tune vector similarity search parameters to strike the perfect balance between recall and latency. Through hands-on labs using tools like SQLite, FAISS, and Annoy, you will experiment with indexing strategies, tune HNSW algorithm parameters, and measure latency, throughput, and resource utilization. By the end of this course, you will be equipped to systematically analyze and optimize production retrieval systems, ensuring your AI applications are not only powerful but also fast and reliable. To successfully complete this course, a familiarity with basic SQL and database concepts and an understanding of vector search principles is recommended.