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Evolution of Search Technologies

Introduction to Search Evolution

Semantic Search

Advancing to Agentic Systems

Large Language Models (LLMs)

Retrieval-Augmented Generation (RAG)

Agentic RAG

Quiz

Keyword Search

Course Feedback

Course Feedback Survey

Welcome to "Evolution of Search: From Keywords to Agentic RAG," a comprehensive course designed to take you on a journey through the transformative history and cutting-edge future of search technologies. In today's data-driven world, finding the right information quickly and accurately is paramount.

Welcome to "Evolution of Search: From Keywords to Agentic RAG," a comprehensive course designed to take you on a journey through the transformative history and cutting-edge future of search technologies. In today's data-driven world, finding the right information quickly and accurately is paramount. This course explores how we moved from basic keyword matching to highly sophisticated, AI-driven agentic systems. We begin by examining the foundations of Keyword Search, understanding its mechanics and limitations. From there, we dive into Semantic Search, exploring how vector embeddings and semantic spaces allow computers to understand the context and meaning behind our queries. As we advance, the course demystifies Large Language Models (LLMs) and their role in revolutionizing information retrieval. You will learn about Retrieval-Augmented Generation (RAG), a breakthrough technique that combines the reasoning power of LLMs with external knowledge bases to provide accurate, hallucination-free responses. Finally, we explore the pinnacle of current search technology: Agentic RAG. Discover how AI agents utilize memory, planning, critics, and retrievers to autonomously navigate complex queries and execute multi-step workflows. Whether you are a developer, a data scientist, or a tech enthusiast, this course will equip you with the knowledge to understand and leverage the next generation of search technologies. Join us to master the evolution of search and stay ahead in the rapidly advancing field of artificial intelligence.

Evolution of Search: From Keywords to Agentic RAG

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            <p>Information retrieval has undergone a radical transformation over the past few decades, evolving from rigid, literal matching systems into highly intuitive, context-aware engines. In the early days of the digital era, keyword search reigned supreme, relying on exact term matching that often struggled with linguistic ambiguity, synonyms, and user intent. As technology progressed, the advent of semantic search revolutionized how we access information by leveraging vector spaces and numerical embeddings. This shift allowed systems to understand the underlying meaning behind a query rather than just the individual words, bridging the gap between human language and machine comprehension.</p>

<p>Building upon these semantic foundations, the rise of Large Language Models (LLMs) has unlocked entirely new paradigms for interacting with data. While LLMs possess vast generative capabilities, they are often constrained by static training data and knowledge cutoffs. To overcome these hurdles, Retrieval-Augmented Generation (RAG) emerged, dynamically sourcing external documents to ground model responses in real-time, verified information. Today, we are witnessing the next frontier: Agentic RAG. By integrating autonomous AI agents capable of planning, tool usage, and iterative reasoning, search is no longer just about retrieving documents—it is about executing complex workflows and solving multi-step problems. This course will guide you through this technological journey, exploring the mechanics, architectures, and future of search systems.</p>
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