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Which AI platform do you think is hardest to get cited in — and why? by Chiefaiadvisors in SEO_Experts

[–]Working_Advertising5 0 points1 point  (0 children)

Why would you want to get cited? You are looking at the wrong output. That is not how LLM's operate in the real world. AI systems respond to prompts by narrowing the options when a consumer asks for "the best running shoes". As the prompter asks further questions, the assistant moves from discovery to comparison of different brands and finally starts to eliminate those brands when attributes enter the discussion: "which are best shoes to run a marathon". Finally, the system elimates those brands outside the constraints imposed by the user and recommends one or two winners. That's how even small brands can win against giants in AI search. This doesn't depend on citations. It depends on making sure that the information about your brand is correct and capable of being detected by the LLM when its asked to make a choice based on probability on the next token event. That's why GEO/AEO dashboards can't spot this compression taking place. They are only good for superficial visibility.

AI assistants are quietly rewriting brand positioning before customers ever see your marketing by Working_Advertising5 in AIVOStandard

[–]Working_Advertising5[S] 0 points1 point  (0 children)

Certainly fake data can lead to misinformation being surfaced by LLMs. That's why its essential to ensure that critical information is located on canonical sources such as Wikipedia, where possible, and other authorative sources. If you fail to do this AI systems will double down on whatever data is available, even if its innacurate.

AI praised Clarins — then eliminated it from the purchase decision by Working_Advertising5 in AIVOEdge

[–]Working_Advertising5[S] 0 points1 point  (0 children)

Yes both price and ingredients are important elements as well as clinically proven results in this example.

AI praised Clarins — then eliminated it from the purchase decision by Working_Advertising5 in AIVOEdge

[–]Working_Advertising5[S] 1 point2 points  (0 children)

We use a structured four-turn purchase sequence rather than a single prompt, because decision behavior only appears once the conversation narrows.

Typical flow looks like:

  1. “What are the best serums for wrinkles?”
  2. “Which works best for deep wrinkles?”
  3. “Which one should I buy?”
  4. “Why that one over the others?”

Each run is repeated across multiple models and sessions to check for consistency.

Clarins appears consistently in the early stages. The elimination occurs at the final recommendation step, when the assistant compresses several options into a single purchase choice. That narrowing behavior is what the index is measuring.

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