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Tired of confident AI answers that crumble under scrutiny? We pull back the curtain on why large language models hallucinate—and how to stop the damage by turning your website into a source AIs can safely cite. Instead of treating models like fact vaults, we treat them like brilliant writers who need trustworthy notes. That shift unlocks a practical playbook: ground responses in real documents, use retrieval‑augmented generation, and structure your content around clear, unambiguous entities.

We break down RAG in plain terms: retrieval first, generation second. Think open‑book exam, where you get to choose the book. When your policies, performance metrics, and definitions are precise and easy to retrieve, AIs pick your pages to anchor their answers. That means replacing “unparalleled performance” with “10,000 records per second, 20% faster than the previous version,” mapping canonical names for products and features, and supporting claims with dates, units, and links. We share cautionary tales—from invented airline policies to fake case law—and translate them into concrete steps any team can take to reduce risk and increase trust.

The bigger win is strategic. As organisations build internal copilots and external chat experiences, they’ll prioritise ingesting domains with reliable, machine‑readable knowledge. This authority economy rewards brands that publish clean, verifiable, entity‑rich content. We walk through a simple content audit you can run this week, how to align claims across your site, and why release notes, policy pages, and structured data make you more “retrievable.” By the end, you’ll know how to write for humans and machines at the same time—and how to become the default reference in your niche.

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"Werq" Kevin MacLeod (incompetech.com)
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