LANDMARK · 6 MIN · USE
Data analysis
After this landmark, you can use AI to interpret and frame data while keeping every decision-driving claim traced back to the data itself.
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This concept is shared, but the Everyday lens is less central here.
You can still explore it. We’re showing the shared explanation and a related practical view without hiding the knowledge.
Asking a model to summarise a report or find patterns in a dataset is a genuinely useful first pass: it frames questions, explains an unfamiliar method, and points at where to look. But fluent analysis is not correct analysis. A model will state a trend, a correlation, or a figure with the same confidence whether it’s in the data or invented — and it can misread what you gave it. Keep it grounded to the numbers you supply, ask it to show where each claim comes from, and check anything that will drive a decision.
This is the canonical concept. It stays the same across learner lenses so personalization never changes the underlying facts.
◌ EVERYDAY LENS
What this looks like for you
Pasting a bill or a report to get the gist is fine. Just treat any specific number it hands back as something to spot-check against the document, not a fact to quote.
MAKE A DECISION
An AI summary of your sales data says “revenue grew 12% quarter over quarter, driven by the north region.” The figure isn’t in your prompt anywhere. What now?
CARRY THISTake one trend an AI reports from your data, find the specific rows behind it, and confirm the claim holds before you repeat it.