AI PRACTICE · 11 STORIES

AI Practice

How models enter real work, without chasing leaderboard noise.

AI Practice6 min read

Before You Run an AI Model Locally, Calculate These Three Costs

Memory is only the entry ticket. Frequency, data sensitivity, and maintenance determine whether local AI is actually useful.

THE ARCHIVE

AI Practice5 min read

Before a model compresses a document, decide how sources, omissions, and uncertainty must appear.

AI Practice5 min read

A four-level method for deciding what can be uploaded, what needs redaction, and what should stay off a general model.

AI Practice5 min read

A small, versioned collection of real tasks reveals more than a generic leaderboard can.

AI Practice5 min read

Text retrieved from email, webpages, and files is untrusted input—even when it looks like ordinary content.

AI Practice5 min read

A reviewer needs authority, evidence, time, and a clear condition for stopping the workflow.

AI Practice5 min read

Provenance records can describe a media file’s history without declaring that every visible claim is true.

AI Practice6 min read

A retention question turns vague privacy concern into a concrete review of copies, accounts, and deletion paths.

AI Practice5 min read

Verify the source, passage, date, and scope before treating an AI-generated citation as support for a decision.

AI Practice6 min read

Route uncertainty, missing evidence, and high-impact outcomes to people before automation turns a weak signal into an action.

AI Practice6 min read

Use approved samples, redaction, and isolation to learn whether a workflow works before exposing sensitive records.