What are you seeing?Here you can open ready-made explanations about what Yolia is doing in your case.
Why it mattersEducational AI should help you understand, never replace data that is actually missing.
How to think about itStart with the big ideas: ask your question first, and then see if the answer points out something you don't already 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 evidence, inference, and the limits of the answer.
Why it mattersConnecting these categories avoids confusing "what Yolia knows in general" with "what she knows specifically about YOUR case."
How to think about itCheck the question, 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 no one measured it.
What are you seeing?Advanced reading explicitly separates observed data from generated inference, and marks the confidence limits of each response.
Why it mattersAn educational response must be traceable to its source and proportional to the real evidence available in the case, never invented.
How to think about itDistinguish variable, mechanism, interaction, uncertainty, limitation, and what additional evidence would improve the answer.
Very simple exampleWhen a piece of information is missing, the correct answer points out the absence and explains why it matters — it doesn't fill in the gap with a made-up number.