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AIReal AI Basics

How we test

Updated June 2026

A review is only worth reading if you can tell how it was made. This page documents the process behind every article on this site, so you can judge whether to trust it.

The four ways an article gets made

Every article carries one of these tags:

  • Hands-on tested, a real person used the tool for its intended job, took screenshots, and documented what happened.
  • Head-to-head, two or more tools were run through the identical task and compared directly.
  • Benchmarked, a measurable test (speed, output quality, cost) was run across tools with the results recorded.
  • Desk-researched, assembled from official docs, pricing pages, and community discussion. No first-hand use. We tell you when this is the case.

What we look at when testing

  1. Setup time, minutes from sign-up to first useful result.
  2. Non-tech friction, anything that requires reading docs, watching a tutorial, or knowing a technical term.
  3. Real-world output, does it actually do the job, or just look like it does?
  4. Free-tier limits, what you can do before paying, and how hard the upsell pushes.
  5. The failure mode, what breaks, and how badly.

The Information Gain score

Every article self-scores on a 9-point rubric that measures how much original work it contains. We refuse to publish anything scoring below 7. Here's the breakdown:

  1. 1.Proprietary data0–2
  2. 2.First-hand evidence0–2
  3. 3.Original framework0–2
  4. 4.Expert attribution0–2
  5. 5.Freshness hook0–1
  6. Total/9

Pages that just rephrase what's already in the top Google results score low and don't get published. Pages with our own screenshots, testing data, and scoring framework score high, and that's the only kind worth making.