How we test AI tools

1. Start from a real task

We pick an everyday job, such as summarizing a 40-page report, writing a week of social posts or cleaning a messy spreadsheet, and define what a good result looks like before we open any tool.

2. Run the same task in every tool

Each tool gets the same input files and the same starting prompt. We then work with it the way a normal user would: follow-up questions, corrections, retries. We note how long it takes to reach a result we would actually use.

3. Check the output

AI tools make confident mistakes. We check facts, numbers and quotes against the source material, and we count the errors we had to fix. A tool that is fast but wrong scores lower than one that is slower but right.

4. Score it

Reviews score tools from 1 to 5 on the criteria that matter for the task, typically:

• Output quality: is the result usable as-is?
• Accuracy: how many errors did we have to fix?
• Ease of use: how quickly can a new user get a good result?
• Value: is the paid plan worth it compared with the free options?

5. Keep it current

AI tools change monthly. Every guide and review shows the date it was last updated, and we re-test our most-read pages when a major model or pricing change lands.

Hands-on reviews and research reviews

Every review is labelled one of two ways. A hands-on review follows the steps above. A research review covers a tool or model that changed recently, before our own tests are done. We build it from the maker's official documentation, pricing and announcements plus published benchmarks, link every source at the bottom of the page, and score what each plan offers on paper. Research reviews never claim we tested something we didn't, and we update them once our hands-on tests are finished.

Money and independence

Some links earn us a commission. That never changes a score or a ranking, and tools cannot pay to be reviewed. See our affiliate disclosure.