Evaluating AI Output
Learn simple checks to judge whether AI output is accurate and safe to use before you act on it.
Confident is not the same as correct
AI writes in a smooth, sure-sounding voice even when it is wrong. A tidy answer is not proof of accuracy. The model can state a fake statistic or invent a quote with total confidence. Your job as a builder is to treat output as a strong draft to verify, not a final truth, especially when the stakes are high or the facts are checkable.
Check the facts that matter
You do not have to verify every word, but you should check the claims that would cause harm if wrong: numbers, names, dates, links, and legal or medical statements. Look for a source, click the citation, or compare against a document you trust. If a claim is important and you cannot confirm it, treat it as unverified until you can.
Spot the warning signs
Some patterns hint that output needs a closer look. Watch for very specific figures with no source, links that do not open, answers that dodge the actual question, or a topic the model is unlikely to know about, like very recent events. When you see these signs, slow down and verify before you rely on the answer.
Build checking into your process
The best teams do not rely on memory to catch errors. They add steps: a human review before anything is sent, a second model or tool to cross-check facts, or a rule that any number must link to a source. Making verification part of the workflow means accuracy does not depend on someone happening to notice a mistake.
Key takeaways
- A confident tone is not proof the answer is correct.
- Verify the high-stakes claims: numbers, names, dates, and links.
- Build review and cross-checks into your workflow, not just memory.
4 questions · pass at 60% to earn XP