AI Keyword Suggestions

A language model’s strength is linguistic coverage. It reliably surfaces the phrasings a team can’t brainstorm: how a parent describes a kids’ game versus how its developer does, the problem-first queries (“can’t sleep”, “stop procrastinating”) that never contain the app’s category name, and audience or context qualifiers you would not think to enumerate. For breaking out of team vocabulary — the single most common keyword-research failure — a good AI pass is worth hours of manual brainstorming.

Its weakness is equally specific: an LLM has no live connection to App Store search data. It cannot know that one plausible synonym carries a popularity of 55 while its twin sits at the floor score, or that a phrase it invented is one no user has ever typed. AI output is a hypothesis list, and hypotheses without validation are how keyword fields fill up with plausible-sounding terms that receive zero searches.

The productive pattern is a funnel: describe the app from a couple of different angles (feature-led, problem-led, audience-led) and generate generously — breadth is cheap. Then cut on truthfulness, since a keyword that misdescribes your app buys installs that churn immediately. Finally, push the survivors through live popularity and difficulty checks; expect a meaningful share to wash out on real demand, and treat the ones that survive as genuine finds.

Two refinements sharpen the loop. First, feed the AI context about competitors — asking for phrasings your rivals’ users might search often produces the gap terms generic prompts miss. Second, rerun generation after metadata updates: your new positioning suggests new angles, and each pass mines a slightly different seam of the language. The loop is cheap enough to repeat quarterly.