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.
An example to consider.
A tool shown working once may still need an understandable recovery path. Try asking what happens when a reader returns midway through the task.
Put it in perspective.
A visible improvement needs a useful comparison. Describe the earlier condition and the task that should become easier, then look for the actual change.
A few starting points
- Ask which conditions the demonstration assumes.
- Look at unusual inputs and interruptions.
- Identify the evidence needed for regular use.
Follow a related question
Choose a nonurgent source.
Choose the moment to checkChoose one task you repeat.
A more intentional digital workspaceKeep learning
Related background to continue exploring this subject.
Google: an introduction to language models NIST: AI risk management frameworkMake room for ideas
