Large Language Models (LLMs) are non-deterministic systems, meaning that when users ask the same natural language questions about data, the query results may vary across different model instances or pricing tiers, as the AI can construct queries in different ways depending on the specific model used and its cost characteristics.
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Grant, LLMs are not deterministic. If users are using natural language to ask AI questions about data, then does that mean the query results are not deterministic?
If you have something that's not deterministic, then the results you get out of it are not deterministic.
So, I'm going to rephrase your question again. Hey, I'm using something that's not deterministic. Does that mean it's not deterministic?
Yes.
That is what that means.
Continues with, "If I ask for month-over-month earnings, then will I always receive the same query result?"
No.
That's what non-deterministic means is that it can build the query in different ways at different times, depending on which model you use, depending on whether you get cheap with the model or whether you get expensive.
And I have absolutely seen that case.
Even with my own queries during some of my training classes, I use AI on screen, and I will ask the same question multiple times to different models and get different results. Like the cheapo model gives me different results than the expensive model. So, welcome to the age of non-determinism.
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