Grounding Bias
A form of secondary bias: how much an AI model defers to, or discounts, retrieved sources once they are grounded into the context, rather than leaning on what it already believed.
Grounding bias is a form of secondary bias. It is how an AI model treats retrieved sources once they are grounded into the context: how much it defers to them, and how much it keeps leaning on what it already believed before the search.
It sits opposite primary bias, the model's pre-retrieval instinct. Where primary bias fires before any source is read, grounding bias is what happens after, when the retrieved grounding snippets either shift the answer or fail to. It is produced during grounding. We surface it by running paired ungrounded and grounded probes and comparing the two, the method behind our Tree Walker.
Mentioned in
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Content Optimization Engine Insights by DEJAN AI
We spent 2.71 Billion tokens teaching a machine to reverse-engineer how AI search ranks pages. What it found overturns the usual SEO playbook: structure and intent beat every credential you've been told to add, and the engine that proved it reached #1 in 1,554 of 2,246 runs.
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Grounding Source ≠ Citation ≠ Mention
Being a cited grounding source does not mean your brand was mentioned in the answer, and being mentioned does not mean you were cited. They are correlated but distinct events produced at different stages of the AI answer pipeline — and conflating them is the source of most AI-visibility measurement
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Primary Bias
Primary bias is what an AI model already believes about your brand before it searches: an ungrounded confidence baked into training that becomes the biggest factor in whether your content is selected in AI answers.