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1. This book discussed AI-Powered Search before AI Search became mainstream
   AI-powered search represents a transformative leap in how we interact with information online, harnessing the capabilities of artificial intelligence to deliver faster, more accurate, and intuitive results. Unlike traditional search engines that rely heavily on keyword matching, AI-powered search works seamlessly with natural language, allowing users to ask questions or input queries as they would in a conversation. It employs sophisticated ranking systems that prioritize content-based relevance, analyzing the meaning and quality of information rather than just matching terms. Additionally, it leverages crowdsourced relevance—drawing insights from user behavior and preferences across vast datasets—to refine and improve search outcomes, ensuring results align closely with what people actually find useful.Beyond these fundamentals, AI-powered search excels through advanced techniques like knowledge graph learning, which connects related concepts and entities to provide richer, more contextual answers. It adapts to domain-specific language by using context, enabling it to understand specialized terminology—whether in medicine, law, or technology—making it invaluable for professionals and hobbyists alike. Semantic search takes this further by interpreting query intent, so a question like “How do I fix a leak?” yields practical steps rather than unrelated articles. Together, these capabilities create a search experience that feels less like digging through data and more like consulting an intelligent, knowledgeable assistant tailored to your needs.

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2. AI-Powered Search for the Professional
   “AI-Powered Search” is an expert walk-through of a domain that is underappreciated in the AI, data science, and machine learning fields. With so much emphasis in the media on AI-powered vehicles, social media, and quantum computing, there’s little to no coverage on the bread-and-butter of machine learning problems: search.The authors of this book have vast experiences to draw on which they reveal in the book’s dense coverage of the various technical and business aspects of AI-powered search. This is a great read for the early-to-mid-level professional who has some exposure to search technology and is looking to take it further.The layout of the book is methodical and orderly. One can easily jump in and cover the first few chapters, then head over to their area of interest. The code examples and details of the application of various search technologies allow you to work from the book on day 1.

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3. A Practical Look at AI-Powered Search
   After reading AI Powered Search by Trey Grainger, Doug Turnbull, and Max Irwin, I found it to be an insightful exploration of how AI is shaping modern search technology. The book explains key concepts like natural language processing, ranking models, and retrieval-augmented generation, showing how these techniques improve search relevance and personalization. The authors do a good job of balancing theory with practical applications, making complex ideas more approachable.What stood out to me was the depth of coverage on user intent, knowledge graphs, and search optimization. The discussions on learning-to-rank models and signals-based improvements were particularly interesting, as they provided real-world strategies for refining search results. However, some sections, especially those focused on deep learning and advanced ranking models, felt dense. While I could follow along, I think beginners might find certain parts challenging without prior exposure to AI concepts. More hands-on examples could have made these topics more accessible.Overall, I found AI Powered Search to be a valuable resource for understanding how AI enhances search engines. It’s well-structured and informative, though best suited for those with some background in AI or search technology.

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4. Learn the variety of approaches for AI Search technologies in one place
   This is a very good book to understand the span of AI search – starts with fundamentals and then into domain specific search with Knowledge Graphs and semantic search. It covers reflected intelligence to take care of the dynamics of changes in search relevancy based on interactions and change of content and finally into how LLMs are revolutionizing search.Loved the concise of author’s writing in explaining difficult concepts and clear clean code that works out of the box and examples outlining the concepts and approach.Highly recommended if you would like to understand the gamut of technologies that is powering the current AI Search space

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5. Search e2e
   Covers wide range of search BM25, Knowledge graph, semantic KG, query intent, ranking, personalization till ANN based search, answer generation and finetuning

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6. Essential book for Search Architects and Engineers
   If you’re looking to elevate existing search solutions with AI-powered enhancements, this guide provides a clear and reliable path to success.

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