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      <video:description>Chunking splits a page into passages small enough to embed and retrieve on their own. How you are chunked decides how you are found: a self-contained passage embeds into a clear point, a fragment that leans on earlier context embeds into a muddy one. Here is what it is and why it decides whether AI can cite you.</video:description>
      <video:player_loc>https://www.youtube.com/embed/pIGRwMjhMaQ</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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    <loc>https://aioinsights.com/lexicon/cosine-similarity/</loc>
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    <changefreq>monthly</changefreq>
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    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/e9U0QAFbfLI/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Cosine Similarity: The Number That Ranks AI Answers | AIO Lexicon</video:title>
      <video:description>Cosine similarity is the cosine of the angle between two embedding vectors, the measure most retrieval systems use to score how close a passage is to a question. Here is how it works and why it decides which content an AI engine cites.</video:description>
      <video:player_loc>https://www.youtube.com/embed/e9U0QAFbfLI</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
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  <url>
    <loc>https://aioinsights.com/lexicon/dense-retrieval/</loc>
    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
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    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/EZfONAne55M/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Dense Retrieval: Why AI Finds You by Meaning, Not Keywords | AIO Lexicon</video:title>
      <video:description>Dense retrieval finds documents by meaning, not by matching keywords. Query and page are embedded into vectors and the system returns nearest neighbors. Here is why a page that never uses the searcher&#x27;s exact words can still be the cited source.</video:description>
      <video:player_loc>https://www.youtube.com/embed/EZfONAne55M</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://aioinsights.com/lexicon/eeat/</loc>
    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/Nrz_4P3QCt4/hqdefault.jpg</video:thumbnail_loc>
      <video:title>E-E-A-T: Google&#x27;s Framework for Judging Source Quality | AIO Lexicon</video:title>
      <video:description>E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trust: Google&#x27;s framework for evaluating who deserves to be believed. It began in the Search Quality Rater Guidelines and is now a working lens for AI trust. Here is what each letter means and why AI engines favor sources that demonstrate it.</video:description>
      <video:player_loc>https://www.youtube.com/embed/Nrz_4P3QCt4</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
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  <url>
    <loc>https://aioinsights.com/lexicon/embeddings/</loc>
    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/viZrOnJclY0/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Embeddings: How Text Becomes Numbers AI Can Compare | AIO Lexicon</video:title>
      <video:description>An embedding turns text into a point in high-dimensional space where nearness equals similarity of meaning. It is the common currency of all AI retrieval: your content is findable only when its embedding lands near the questions customers ask. Here is what a vector is and why it decides whether AI can cite you.</video:description>
      <video:player_loc>https://www.youtube.com/embed/viZrOnJclY0</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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    <loc>https://aioinsights.com/lexicon/entity-seo/</loc>
    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/XBGhmgor_S4/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Entity SEO: From Strings to Things, and Why AI Needs It | AIO Lexicon</video:title>
      <video:description>Entity SEO optimizes so search and AI systems recognize your business as a well-defined thing, not a bag of keywords: consistent naming, clear relationships, corroboration, and sameAs links. Here is how it works and why it decides whether AI can name you.</video:description>
      <video:player_loc>https://www.youtube.com/embed/XBGhmgor_S4</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
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  <url>
    <loc>https://aioinsights.com/lexicon/geo/</loc>
    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/TkmRinmgYj8/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Generative Engine Optimization (GEO): Getting Cited by AI | AIO Lexicon</video:title>
      <video:description>Generative Engine Optimization (GEO), named in 2023 research by Aggarwal et al., is the discipline of making a brand visible inside AI-generated answers. It reframes optimization around being retrieved, cited, and synthesized rather than ranked. Here is what GEO is and how it extends SEO.</video:description>
      <video:player_loc>https://www.youtube.com/embed/TkmRinmgYj8</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://aioinsights.com/lexicon/share-of-model/</loc>
    <lastmod>2026-07-20</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/8xOyMQGygpo/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Share of Model (SoM): The New AI Visibility Metric | AIO Lexicon</video:title>
      <video:description>Share of Model measures how often, how prominently, and how favorably a brand appears in AI-generated answers, relative to competitors. Coined at Jellyfish in 2023. Here is what it is, why AI answer variance makes it easy to fake, and what a defensible share figure must publish.</video:description>
      <video:player_loc>https://www.youtube.com/embed/8xOyMQGygpo</video:player_loc>
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      <video:live>no</video:live>
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    <loc>https://aioinsights.com/lexicon/knowledge-graph/</loc>
    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/YVLwu3jE09c/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Knowledge Graph: The Entity Network Behind AI Recommendation | AIO Lexicon</video:title>
      <video:description>A knowledge graph is a structured network of entities and the relationships between them. It is the substrate behind Google&#x27;s Knowledge Panel and how engines decide who you are. Here is how a business becomes a recognized node and why that decides AI recommendation.</video:description>
      <video:player_loc>https://www.youtube.com/embed/YVLwu3jE09c</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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  <url>
    <loc>https://aioinsights.com/lexicon/llmo/</loc>
    <lastmod>2026-07-10</lastmod>
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    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/6XqbPUvz5gk/hqdefault.jpg</video:thumbnail_loc>
      <video:title>LLMO (Large Language Model Optimization): Shaping What AI Knows About You | AIO Lexicon</video:title>
      <video:description>Large Language Model Optimization (LLMO) is the practice of shaping how AI models represent, recall, and recommend your brand, across both their trained memory and the sources they retrieve live. Here is what it is and why it decides whether AI can find and vouch for you.</video:description>
      <video:player_loc>https://www.youtube.com/embed/6XqbPUvz5gk</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
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    <video:video>
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      <video:title>llms.txt: The File That Hands AI a Map of Your Site | AIO Lexicon</video:title>
      <video:description>llms.txt is a proposed standard: a markdown file at your site root that offers AI systems a clean, curated map of your most important content. Here is what it is, where it came from, and why adoption is still partial but the upside is real.</video:description>
      <video:player_loc>https://www.youtube.com/embed/XOo6YC6n2hE</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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    <loc>https://aioinsights.com/lexicon/query-fan-out/</loc>
    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
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    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/lEtXlk0XTsI/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Query Fan-Out: How Google AI Mode Splits One Search Into Many | AIO Lexicon</video:title>
      <video:description>Query fan-out is the technique behind Google's AI Mode: one query is decomposed into many related sub-queries, run in parallel across the web, the Knowledge Graph, structured data, and shopping, then synthesized into a single answer. Here is how it works and why you now compete to be the best passage for dozens of hidden sub-questions.</video:description>
      <video:player_loc>https://www.youtube.com/embed/lEtXlk0XTsI</video:player_loc>
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      <video:live>no</video:live>
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  <url>
    <loc>https://aioinsights.com/lexicon/rag/</loc>
    <lastmod>2026-07-10</lastmod>
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    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/T-D1OfcDW1M/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Retrieval-Augmented Generation (RAG): The Architecture Behind AI Answers | AIO Lexicon</video:title>
      <video:description>Retrieval-Augmented Generation (RAG) is the dominant architecture behind AI answers: retrieve relevant passages from a corpus, then feed them to a language model to compose a grounded, cited response. It makes AI visibility a retrieval problem first. If you are not retrieved, you are not in the answer.</video:description>
      <video:player_loc>https://www.youtube.com/embed/T-D1OfcDW1M</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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    <loc>https://aioinsights.com/lexicon/reranking/</loc>
    <lastmod>2026-07-10</lastmod>
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    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/aVgZE746OXw/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Reranking: The Second Pass That Decides Which Passages Win | AIO Lexicon</video:title>
      <video:description>Reranking is a second, more precise retrieval pass that re-scores a shortlist of passages by reading the query and each passage together. Here is how retrieve-then-rerank works, and why a page can only be reranked if it was retrieved first.</video:description>
      <video:player_loc>https://www.youtube.com/embed/aVgZE746OXw</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
    </video:video>
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    <loc>https://aioinsights.com/lexicon/retrieval-legibility/</loc>
    <lastmod>2026-07-10</lastmod>
    <changefreq>monthly</changefreq>
    <priority>0.8</priority>
    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/LXF8bM4g-J4/hqdefault.jpg</video:thumbnail_loc>
      <video:title>Retrieval Legibility: Can a Machine Actually Read Your Facts? | AIO Lexicon</video:title>
      <video:description>Retrieval legibility is how easily a machine can read, parse, and embed the actual facts on your page. A price locked in an image or a claim buried in JavaScript is invisible to a retriever, and illegible facts cannot be cited however true they are.</video:description>
      <video:player_loc>https://www.youtube.com/embed/LXF8bM4g-J4</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
      <video:live>no</video:live>
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    <loc>https://aioinsights.com/lexicon/sbert/</loc>
    <lastmod>2026-07-10</lastmod>
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    <video:video>
      <video:thumbnail_loc>https://i.ytimg.com/vi/O3xbVmpdJwU/hqdefault.jpg</video:thumbnail_loc>
      <video:title>SBERT (Sentence-BERT): The Model Behind Semantic Search | AIO Lexicon</video:title>
      <video:description>SBERT (Sentence-BERT) turns whole sentences into fixed-length vectors so machines can measure meaning by distance. It is the retrieval engine underneath AI search, semantic search, and RAG. Here is what it is and why it decides whether AI can find you.</video:description>
      <video:player_loc>https://www.youtube.com/embed/O3xbVmpdJwU</video:player_loc>
      <video:family_friendly>yes</video:family_friendly>
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    <lastmod>2026-07-10</lastmod>
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      <video:title>Structured Data (Schema.org / JSON-LD): Stating Facts a Machine Can Read | AIO Lexicon</video:title>
      <video:description>Structured data is machine-readable markup that states facts about your business explicitly: name, type, address, ratings, services. Delivered as JSON-LD using the Schema.org vocabulary, it removes ambiguity and feeds knowledge graphs and AI answers. Here is what it is and why it decides whether AI can trust you.</video:description>
      <video:player_loc>https://www.youtube.com/embed/MF9lS3UU0ww</video:player_loc>
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      <video:description>A vector database stores embeddings and finds nearest neighbors at scale, using ANN indexes like HNSW to search millions of vectors in milliseconds. It is the memory an AI engine reaches into for sources. Here is how it works and why it decides whether AI can cite you.</video:description>
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