What are you seeing?Here you can open ready-made explanations of what Yolia is doing in your case.
Why it mattersThe educational AI should help you understand, never substitute for data that is actually missing.
How to think about itStart with the big picture: ask your question first, then check whether the answer points to something you still don't know.
Very simple exampleIf there is no measurement, an explanation can teach you what that data means, but it will never magically turn it into actual measured data.
What are you seeing?The explanatory layer separates general knowledge, case-specific evidence, inference, and the limits of the answer.
Why it mattersConnecting these categories keeps you from confusing "what Yolia knows in general" with "what it knows specifically about YOUR case."
How to think about itCheck the question, the educational answer, whether it is data or inference, and the Library link if you want to go deeper.
Very simple exampleAn answer can explain perfectly what VPD is in general, and still not know the real VPD of your case if no one measured it.
What are you seeing?The advanced reading explicitly separates observed data from generated inference, and marks the confidence limits of each answer.
Why it mattersAn educational answer must be traceable to its source and proportional to the real evidence available for the case, never made up.
How to think about itIt distinguishes variable, mechanism, interaction, uncertainty, limitation, and what additional evidence would improve the answer.
Very simple exampleWhen a data point is missing, the correct answer flags the absence and explains why it matters — it does not fill the gap with a made-up number.