A prompt that states the work
A clear request gives the output a purpose that a person can evaluate.
Notes on making sense of the things around us
The notebook.
A journal about the judgment around AI-generated material. We explore evidence, uncertainty, and the responsibilities that remain after a tool produces a fluent result.
11 stories to explore
Capture the question first
A useful note can preserve the uncertainty that made an idea interesting.
A small AI task with a clear stop
A bounded experiment gives both the tool and the reviewer a manageable responsibility.
Where automation should hand back
A process needs a clear way to return an uncertain decision to a person.
Human review needs a specific job
A person can only provide useful oversight when the review task is clear.
A time-zone note that travels
A meeting time becomes easier to share when its assumptions come with it.
Check an AI answer in pieces
A structured review can make a large response easier to assess.
A thoughtful view of what comes next
Look for assumptions, evidence, and the different paths an idea might take.
Examples shape the model’s task
The choice and labeling of training data influence what a system can learn.
Examples that make an instruction concrete
A representative example can clarify the pattern you want a tool to follow.
Context is an input with a boundary
A tool can only use the information actually available to its current process.
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A small idea to take with you
Find the claim that needs evidence
Read a generated explanation and choose its most important factual claim. Identify the original source or observation that would support it.