This episode is part of the AI Summary series covering the AI Search Manual chapter by chapter. Chapter 6 traces the evolution of information retrieval from simple lexical matching to today’s neural systems that power generative search.We start with the foundations of inverted indexes and lexical search, which drove early SEO practices like exact keyword targeting. The episode then explores the rise of embeddings, where meaning is captured in vector space, enabling systems to connect related terms and concepts beyond surface-level matches.We discuss how Google now embeds not just words and documents but entire websites, authors, entities, and users, creating a high-dimensional map of relevance. The introduction of transformers, BERT, GPT, and later MUM reshaped retrieval into a multimodal and multilingual process, capable of reasoning across text, images, and more. We also cover Muvera, a breakthrough in scaling multi-vector retrieval efficiently, and why embeddings have become the universal language of AI-driven search.For brands, the shift is clear: content visibility depends on semantic alignment, structured depth, and occupying the right neighborhoods in embedding space so that generative systems surface your work in synthesized answers.Read the full chapter at ipullrank.com/ai-search-manual

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