Optimize SQL and Vector Search Parameters

Overview

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.

Syllabus

SQL Query Optimization

This module provides essential techniques for writing secure and efficient SQL to diagnose performance issues in production systems. You will learn why query optimization is critical, what parameterized queries are, and how to construct them to retrieve performance data safely. You will conclude by applying these skills in a hands-on lab.

Vector Search Parameter Tuning

This module focuses on the core challenge of vector search: balancing accuracy and speed. You will learn why this trade-off is fundamental, what key ANN algorithm parameters control it, and how to methodically tune them. You will apply this knowledge in a lab to find the optimal balance for a given use case.

Performance Benchmarking and Analysis

In this module, you will learn how to build a systematic framework for performance testing. You will discover why continuous benchmarking is superior to one-off tests, what key metrics to measure, and how to create an automated suite to ensure your system remains performant over time.