What a Practical Team AI Use Policy Should Cover
A practical framework for allowed work, data boundaries, approved accounts, human approval, verification, incidents, and review cycles in a team AI policy.
Guides
How to Build a Small Evaluation Set for AI Tools
A practical method for turning representative work, failure cases, acceptance criteria, and review evidence into a small repeatable AI evaluation set.
How to Evaluate an AI Tool in 30 Minutes
A time-boxed evaluation scenario for testing representative work, failure cases, evidence quality, operating fit, and stop conditions before adoption.
What to Check About Data Export and Vendor Lock-In Before Subscribing
A practical guide to testing whether AI data, settings, workflows, and evidence can actually move before a subscription becomes difficult to leave.
AI Tool Operating Cost Matters More Than Feature Count
A practical method for calculating the full operating cost of an AI tool, including usage, tools, retries, human review, integration, reliability, migration, and unit economics.
8 Signals to Check When Reading AI Product Reviews
A practical framework for judging whether an AI product review is independent, current, reproducible, evidence-backed, and relevant to the exact plan and workflow you are considering.
How to Audit Overlapping AI Subscriptions
A practical audit for reducing duplicate AI subscriptions by measuring real workloads, paid-only value, account boundaries, portability, cancellation timing, and fallback risk.
How to Use AI Summaries Without Losing the Original Source
A layered reading workflow that uses AI summaries as navigation aids while preserving document identity, evidence links, exceptions, uncertainty, and original-source review.
Why You Should Not Trust an AI-Generated Comparison Table Without Checking It
A practical method for checking candidate selection, definitions, dates, missing values, evidence, weighting, and ranking sensitivity in AI-generated comparison tables.
How to Reduce AI Hallucinations with Better Prompts and Verification
A practical prompt and verification method for limiting unsupported AI claims by defining evidence, separating uncertainty, grounding answers, checking citations, and testing repeated tasks.
Free vs Paid AI Plans: A Practical Upgrade Decision
A practical framework for deciding whether a free AI plan is enough or a paid subscription is justified by workload, limits, privacy, administration, reliability, and total cost.
AI Tool Privacy and Security Checklist Before Adoption
A practical pre-adoption checklist for classifying data, reviewing provider controls, limiting access and agency, testing prompt injection, and preparing incident response.
How to Verify AI Answers, Sources, and Factual Claims
An eight-step process for checking AI-generated claims, quotations, numbers, and sources through risk ranking, primary evidence, dates, cross-checking, and human approval.
7 Criteria to Check Before Choosing an AI Tool
A practical seven-part checklist for evaluating AI tools by use case, reliability, privacy, workflow fit, cost, portability, and long-term value.