Datasets, labeling & ground truth

What this lesson shows

Ground truth is built by people: clear guidelines, labelers, and curated gold sets.

The question it opens with

Your eval is only as good as its answers. Where does 'the right answer' even come from?

The walkthrough, in the lesson's own words

• You're in the labeling chair. Read this ticket and tag it, then reveal what others picked.
• You're the labeler now. Tag each ticket, then reveal what others picked.
• Drag in a clear guideline and watch each ticket settle on a single decided answer, which may overrule the majority.
• Quality beats quantity: trustworthy AI rests on trustworthy labels.
• Every eval grades against a 'right answer.' Before we ask where it comes from, try producing one yourself.
• Notice: reasonable people split on this ticket. The 'right answer' isn't sitting in the data, people decide it by labeling, one judgment call at a time. Next: do a few more.
• On each ticket two pick one label and one picks another. Low agreement isn't lazy labelers; it means nobody told them how to handle the tricky cases. A clear guideline is what raises it.
• Toy panel of 3 labelers; the percentages show the trend, not a measured study.
• A guideline is the written rulebook: 'a money problem mixed with anything else → Billing.' Edge cases get a decision, so labelers stop guessing.
• A model drafts every label in seconds, but a human still verifies each one. The machine removes the typing, not the judgment.
• No. However the labels get drafted, the rule stays the same: the machine proposes, a person confirms or fixes it before it counts as gold.
• Models can pre-label, but humans verify. Ten thousand sloppy labels lose to a hundred clean ones, because everything built on top of them, every eval, every judge, every model trained on them, inherits their quality. Trustworthy AI rests on trustworthy labels.
• Your team built an eval set fast by having three contractors tag tickets with no shared rulebook, and the scores feel random. What went wrong?
• Without a clear guideline, labelers disagree on the ambiguous cases, so inter-annotator agreement is low and the 'right answer' is shaky. Write a guideline that decides the edge cases, have multiple labelers adjudicate to a gold set, and let a model pre-label only if a human still verifies each one.

Key takeaway

Trustworthy AI rests on trustworthy labels; good data work is the hidden foundation.

What you can do after this lesson

You can explain how labeled datasets and ground truth are built, and why label quality is critical.

Where it leads: And that data doesn't sit still, production turns it into a flywheel.

This is the written summary. The lesson itself is interactive: you predict, drag and operate the mechanism above, and the reveal answers you.