"LLM training data is fundamental to optimizing AI answers, even if the platform runs real-time searches, because the fan-out components stem from what the model already knows. For example, if the training data indicates that a business is an organic skincare brand, the fan-out component might search for certifications. Citations AI answers often include citations (URLs of sources), which come from live searches, not training data. LLMs do not store URLs."
"Citations (i) are branded responses that may influence buying decisions and (ii) likely impact the training data containing info on a brand. Thus citations are key to AI optimization. A consumer considering a skincare brand may prompt Google's AI Mode for reviews and certifications. The response will likely contain sources. Here's an example prompt addressing The Ordinary, a skincare brand: Is The Ordinary skincare good and certified? AI Mode's answer included an advisory warning from the U.S. Food and Drug Administration, as well as links to a magazine article and influencer posts that questioned the ingredients."
AI-driven search outcomes depend on both what large language model training data contains about a company and what live searches reveal during queries. LLM training data shapes the fan-out of follow-up searches and influences which nearby signals the model prioritizes. Live-search citations supply URLs and branded sources that can shape buying decisions and feed back into future training corpora. Brands cannot fully control third-party sentiment but can increase citation likelihood by addressing concerns on-site, creating targeted FAQ pages, and publishing certification and review responses. Optimizing for AI requires managing both training-data signals and cited live-source content.
Read at Practical Ecommerce
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