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In this solo advisory video, Julie Broad explains why large language models such as Claude, ChatGPT, and Grok often fail to surface an author’s book. She identifies five recurring issues that act as negative signals for AI recommendation engines and offers concrete remediation steps for each. The first problem is the lack of public discussion—independent mentions are the strongest trust signal, so authors should concentrate reader conversations in a single, signature‑tagged venue. The second problem is the absence of an “entity layer,” meaning no authoritative web records confirming the book’s existence. The third problem is inconsistent name spelling across social‑media properties; authors must standardize a single spelling across all profiles. The fourth problem is that the author’s framework or method has no named home; naming the process and creating a dedicated webpage provides a strong ownership signal. The fifth and most critical problem is unverifiable proof—claims without traceable links are ignored by AI, so every logo on a media page must link to the original source, and missing links should be removed. By addressing these five pillars—public discussion, entity layer, name consistency, framework naming, and verifiable proof—authors can improve the likelihood that LLMs will recommend their books.

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