A prompt that states the work
A clear request gives the output a purpose that a person can evaluate.
Ideas to observe, question, and put into practice
Ideas in practice.
A journal about the foundations of everyday AI. We explore models, context, and evaluation in language that connects a technical idea with a practical question.
10 stories to explore
Finding a source and generating a sentence
Retrieving information and composing an answer are different parts of an application.
Build with a little more restraint
Useful experiences can begin by asking what actually needs to be sent, stored, and run.
Prediction and the appearance of understanding
A fluent response can be useful without explaining how the model reached it.
Compare groups that share a start
A cohort can make a comparison clearer by naming a common starting condition.
Examples shape the model’s task
The choice and labeling of training data influence what a system can learn.
Try a big idea on a small scale
A bounded experiment can make an abstract possibility easier to evaluate.
Human review needs a specific job
A person can only provide useful oversight when the review task is clear.
A plausible answer can still be wrong
Confidence in the wording is different from evidence for the claim.
No stories here just yet.
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A small idea to take with you
Separate two parts of the process
Choose an AI application and distinguish its input, model output, and any retrieved sources. Which part would you need to inspect for your task?