What you are seeingHere 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 ideas: ask your question first, and then check whether the answer points to something you don't know yet.
Very simple exampleIf a measurement doesn't exist, an explanation can teach you what that data point means, but never magically turn it into real measured data.
What you are seeingThe 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 itReview the question, the educational answer, whether it is data or inference, and the link to the Library 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 nobody measured it.
What you are seeingThe 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 in the case, never invented.
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 points out the absence and explains why it matters — it does not fill the gap with an invented number.