ISSUE 001 DIGITAL LIFE OBSERVER NETWORK · ONLINE
FACET Technology & Digital Life

AI PRACTICE · 11 STORIES

AI Practice

How models enter real work, without chasing leaderboard noise.

Abstract desktop computer balancing performance, privacy, and operating cost

AI Practice9 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.

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THE ARCHIVE

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Layered pages and editorial marks showing a source-to-summary verification process

AI Practice8 min read

An AI Summary Is Not an Answer: Build a Verifiable Reading Workflow

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

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Four nested zones around a document representing levels of data sensitivity

AI Practice8 min read

Draw a Data Boundary Before You Give a File to AI

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

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A grid of evaluated AI outputs marked with requirements, evidence, and review status

AI Practice9 min read

Build an AI Evaluation Set From Work You Already Understand

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

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A document carrying a hidden instruction reaches an AI tool boundary and permission gate

AI Practice10 min read

A Document Can Contain Instructions for Your AI: Treat Prompt Injection as a Security Boundary

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

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An AI workflow stops at a human review gate showing evidence and approval controls

AI Practice9 min read

“Human in the Loop” Is Too Vague: Define the Review Checkpoint

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

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A digital media file linked to a signed chain of provenance records and verification states

AI Practice9 min read

What Content Credentials Can Prove—and What They Cannot

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

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A document moving through a controlled retention timeline with storage and deletion checkpoints

AI Practice9 min read

Before You Upload a Document to AI, Ask Where It Will Be Retained

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

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Research answer connected to original source documents and highlighted verification checkpoints

AI Practice9 min read

Citations From an AI Answer Are Starting Points, Not Evidence

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

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AI workflow branching to an escalation queue when evidence is incomplete or outcome is high impact

AI Practice9 min read

When an AI Workflow Should Escalate Instead of Decide

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

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Private document test environment separated from production records by a review boundary

AI Practice9 min read

Test an AI Tool With Private Documents Without Treating Them as Test Data

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

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