Context is an input with a boundary
A tool can only use the information actually available to its current process.
Observations, explanations, and small discoveries
The fieldbook.
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.
12 stories to explore
An outlier is a question first
An unusual value deserves context before it is removed or celebrated.
Examples shape the model’s task
The choice and labeling of training data influence what a system can learn.
Notes you can find again
A note becomes more useful when it has enough context to survive the moment.
A benchmark measures a defined task
A score becomes meaningful through the test that produced it.
A dashboard with a question beside it
A useful collection of charts makes its purpose easier to see.
Check an AI answer in pieces
A structured review can make a large response easier to assess.
Instructions with an example
A small worked example can make an unfamiliar task easier to begin.
A small AI task with a clear stop
A bounded experiment gives both the tool and the reviewer a manageable responsibility.
A dictionary for your columns
Short definitions can prevent a table from depending on one person’s memory.
Training and use are different stages
Learning a model’s parameters and producing a response have different roles.
Use a draft to find the next question
Generated wording can help expose a missing detail before it becomes a finished piece.
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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?