Testing AI for feature-to-benefit mapping

I’m a product description specialist and I’ve been pairing a features spreadsheet (columns: specs, use-case, customer claim) with Claude to generate benefit-led bullets, then running the drafts through Hemingway for Grade 6 readability. On a 50-SKU pilot last week, time per page dropped from 22 to 9 minutes without losing brand voice; anyone using a better combo or a term base tool that plugs into this flow?

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I keep a tiny “do/don’t” term base as a CSV (preferred, banned, tone notes) and feed it with the brief, then have Claude reference it explicitly in the bullets. For enforcement, Vale (https://vale.sh) catches term slips faster than a second Hemingway pass — just watch that Grade 6 everywhere can flatten voice.

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Quick example: I have Claude draft each bullet as ‘benefit — because [proof: SPEC_ID]’ (e.g., ‘Faster setup — because pre‑paired BT 5.3 [P3]’) and I strip the bracketed proof before publish; it’s quicker for QA since PM/dev can spot gaps fast. Small caveat: Hemingway G6 is solid, but I often see ‘Grade 7–8’ win A/Bs for higher-ticket or B2B.

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I swapped Hemingway for a quick Vale pass with a small ‘claims-safe’ column in the sheet; it flags absolutes and tone slips and still keeps me at about 9 min/page. I also have Claude add a one‑line ‘so what’ per spec and cap bullets at 12 words — keeps benefits tight when stakeholders try to sneak features back in, . If you want more control, @clara_lee85’s term base + a Vale ruleset (https://vale.sh) has been the most reliable combo for me.

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I’ve had good luck adding a hidden “JTBD hook” column and prompting Claude to start each bullet with the outcome, then append a one‑line “so what?” that I cut if it reads redundant. To keep claims tidy, I have it output a 1–5 risk score based on modals/comparatives and only ship ≤2. For readability, a quick pass with write‑good (GitHub - btford/write-good: Naive linter for English prose) catches fluff fast — it’s a lint roller for phrasing.

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