In one sentence
Ground truth is the agreed correct answer for a case, used to judge whether the AI got it right.
The simple model
To measure an AI's output, you need something to compare it against. Ground truth is that something: the known correct result for each case, decided by someone who understands the work. If the ground truth for an email is "billing", and the AI says "support", that is a measurable error.
Without ground truth, you can only say the output "looks reasonable", which is not a measurement.
Why you care
Evaluation is only as good as its ground truth. If the correct answers are wrong or sloppy, your measurements are meaningless, and you may reject a good workflow or trust a bad one. Establishing solid ground truth is quiet, unglamorous work that determines whether all your testing means anything.
Getting ground truth right
- Have someone who knows the work decide the correct answer, not whoever built the workflow.
- Use real past cases where the outcome is already known where you can.
- Be honest about genuine ambiguity. If two experts disagree on the right answer, the task itself is ambiguous, and that is important to know. Record it rather than forcing a single answer.
A small example
A team measured a classifier as only 70 percent accurate and nearly abandoned it. Reviewing the ground truth, they found many "errors" were cases where the labels themselves were inconsistent. Once the correct answers were agreed properly, the classifier scored far higher, and the real weak spots stood out clearly.
What this does not solve
Ground truth lets you measure accuracy; it does not make the task easy or remove ambiguity. Some cases have no single right answer. Good ground truth captures that honestly, which leads to better design, rather than a falsely precise score.
