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this post was submitted on 22 Jan 2026
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Likely just hallucinations. For example, there is no way they would store a confidence score as a string
It's also possible that it retrieved the data from whatever sources it has access to (ie as tool calls) and then constructed the json based on its own schema. That is, the string value may not represent how the underlying data is stored, which wouldn't be unusual/unexpected with llms.
But it could definitely also just be a hallucinations. I'm not certain, but since it looks like the schema is consistent in these screenshots, it does seems like the schema may be pre-defined. (But even if this could be verified, it wouldn't completely rule out the possibility of hallucinations since grok could be hallucinating values into a pre-defined schema.)
yea the only way I can see confidence being stored as a string would be if the key was meant for a GUI management interface that didn't hardcode possible values(think for private investors or untrained engineers for sugar/cosmetic reasons). In an actual system this would almost always be a number or boolean not a string.
Being said, its entierly possible that it's also using an LLM for processing the result, which would mean they could have something like "if its rated X or higher" do Y type deal, where the LLM would then process the string and then respond whether it is or not, but that would be so inefficient. I would hope that they wouldn't layer like that.
If it were hallucinations which it very well could be, it means the model has learned this bias somewhere. Indicating Grok has either been programmed to derank Palestine content, or Grok has learned it by himself (less likely).
It's difficult to conceive the AI manually making this up for no reason, and doing it so consistently for multiple accounts so consistently when asked the same question.
If you understand how LLMs work it's not difficult to conceive. These models are probabilistic and context-driven, and they pick up biases in their training data (which is nearly the entire internet). They learn patterns that exist in the training data, identify identical or similar patterns in the context (prompts and previous responses), and generate a likely completion of those patterns. It is conceivable that a pattern exists on the internet of people requesting information and - more often than not - receiving information that confirms whatever biases are evident in their request. Given that LLMs are known to be excessively sycophantic it's not surprising that when prompted for proof of what the user already suspects to be true it generates exactly what they were expecting.