Building Semantic Search Systems with Large Language Models

Traditional keyword search often misses the true intent behind a user's query. By leveraging Large Language Models (LLMs) and semantic search, you can build systems that understand context, meaning, and relationships within text to deliver highly relevant results. In this text-only course, you will transition from understanding basic search concepts to implementing modern information retrieval systems. You will learn how to represent text as dense vectors, re-rank search results for maximum relevance, and generate concise summaries from retrieved documents. What you'll learn: - Understand the core concepts of semantic search, text embeddings, and vector spaces - Compare dense retrieval methods with traditional keyword-based search techniques - Implement reranking strategies to improve the precision of search results - Connect search queries to vector databases for efficient document retrieval - Apply Retrieval-Augmented Generation (RAG) patterns to summarize search findings - Design search workflows that handle natural language queries with high accuracy This course begins with foundational terminology, explaining how computers represent meaning through numbers, before guiding you through the architecture of modern semantic search systems. You will read clear explanations and analyze practical code snippets designed to help you construct your own retrieval pipelines. This course is designed for software developers, data enthusiasts, and beginners looking to enter the field of AI-powered search. No prior experience with natural language processing is required. Start reading today to unlock the power of semantic search in your applications.