Vector Storage and Retrieval Evaluation for LLM Systems

Building reliable AI applications requires more than just calling an LLM API; it demands high-quality data retrieval. Understanding how to represent text as vector embeddings and store them efficiently is the foundation of modern search and retrieval-augmented generation (RAG) systems. This text-based course guides you through the entire lifecycle of vector data management. You will progress from understanding the fundamentals of semantic search to setting up database storage and implementing rigorous evaluation strategies to ensure your retrieval system performs accurately in production. What you will learn: Understand the core concepts of text embeddings, vector spaces, and semantic similarity; Apply effective text chunking strategies to prepare your data for high-quality embedding generation; Configure and manage vector storage using pgvector in a PostgreSQL database; Implement hybrid search techniques by combining traditional keyword queries with vector-based semantic search; Create golden datasets to systematically test and measure the accuracy of your retrieval pipeline; Evaluate search performance using key LLMOps metrics to continuously improve retrieval quality. You will start with foundational terminology and vector theory before moving into hands-on database configuration and querying. Finally, you will explore advanced evaluation techniques to measure and optimize your system's performance using realistic data scenarios. This course is designed for software developers, data enthusiasts, and aspiring AI engineers who are new to LLMOps and vector databases. No prior experience with vector search or machine learning is required, though a basic familiarity with database concepts and Python is helpful. Step into the world of LLMOps and start building reliable, evaluated retrieval systems today.