How We Research Our Guides

Every role guide starts with occupational data, not guesswork. We ground each role in U.S. Department of Labor O*NET data: the tasks the role actually performs, the software it actually uses, and how it spends its week. On top of that we layer real-world research: day-in-the-life accounts, practitioner communities, and the pain points people in the role describe in their own words.

From workflow to guide

We map each role's weekly workflow, rank its pain points, and match them to AI capabilities that exist today in off-the-shelf tools. Each match becomes a guide at one of four levels, from a single copy-paste prompt to a multi-step automation. Guides use the role's own terminology and realistic scenarios, and every recommended tool is chosen for that role rather than promoted generically.

Keeping guides current

AI tools change monthly, so volatile details like pricing, plan names, and recommended models resolve from a continuously updated tool registry rather than being frozen into the text. We scan tool changelogs for breaking changes, flag affected guides for rewrites, and record updates in each role's changelog.

No tool vendor pays for placement in our rankings.