This episode is part of the AI Summary series covering the AI Search Manual chapter by chapter. Chapter 7 takes a deep dive into the architecture of leading AI search platforms, breaking down how each system retrieves, ranks, and synthesizes information.We start with the core Retrieval-Augmented Generation (RAG) pattern, which grounds large language models in real-time data. The episode explains embedding-based indexing, hybrid pipelines that blend lexical and semantic retrieval, and why passage-level clarity and extractability are now as important as keyword targeting.We then compare Google AI Overviews and AI Mode, ChatGPT with browsing, Bing Copilot, and Perplexity AI. Each has its own approach to query understanding, reranking, and citation, which means the levers for visibility differ by platform. Google rewards breadth of coverage and multi-intent relevance, Bing favors hybrid SEO strength and clean passages, Perplexity emphasizes clarity and transparency, while ChatGPT depends on real-time accessibility.The discussion closes with a platform-by-platform GEO playbook, showing how retrievability, extractability, and trust signals form the consistent sequence of gates every brand must pass to appear in AI-generated answers.Read the full chapter at ipullrank.com/ai-search-manual

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