A demonstration can reveal a promising capability. It usually shows one situation, with particular inputs and conditions. Everyday use includes interruptions, unusual cases, and changing needs.
Look for evidence about those conditions. Can the system explain its limits? How does it fail? What does it need from the surrounding workflow?
A thoughtful evaluation can appreciate the possibility while keeping the unanswered questions visible. That distinction gives the idea a more useful path forward.
- Ask which conditions the demonstration assumes.
- Look at unusual inputs and interruptions.
- Identify the evidence needed for regular use.
Picture this situation.
A tool shown working once may still need an understandable recovery path. Try asking what happens when a reader returns midway through the task.
A second way to look.
Consider the point where a person should regain control. A future tool remains useful when its limits are legible and a different choice remains possible.
Follow a related question
Try a small offline task.
When a tool keeps working offlineCompare one object under two light sources.
A color depends on the lightKeep learning
Related background to continue exploring this subject.
Google: an introduction to language models NIST: AI risk management framework