Figr Design Blog

It’s a copy-editing prompt for AI that treats blog drafts as keyword-sensitive: preserve all core wording and structure, only fix mechanics, lightly humanize, and add a few conversational inline questions. It must respect headings, spacing, and code blocks, avoid em/en dashes, keep lists/tables intact, and convert bare URLs and named entities into clean, canonical Markdown links without inventing sources.

Most AI tools that auto-generate Jira tickets just turn ideas into nicely formatted to-dos, but strip out the real payload: problem context, constraints, rationale, and clear acceptance criteria. The real unlock is treating tickets as executable specifications derived from rich, component-mapped design specs, so developers can ship without clarification rounds. Teams that do this systematically reclaim a big chunk of wasted engineering time and see velocity rise without working harder.

Most AI release note tools just reformat commit logs, so you get neat documentation that barely moves feature adoption. The real job is to segment by user type, translate technical changes into clear benefits, and push contextual, in-app, measurable announcements that answer “what can I do now that I couldn’t before?”. If your release notes aren’t lifting usage, they’re documentation theatre, not product communication.

Most teams treat pricing pages as static A/B test surfaces, tweaking visuals while ignoring decision psychology, friction, and the full funnel from pricing to payment. The next wave of tools (and approaches) use AI to diagnose where and why users hesitate, apply proven pricing-page patterns, and optimize checkout and onboarding, not just the page itself. If your trial-to-paid or self-serve conversion is stuck, the fix is usually structural and psychological, not another button color test.

Most in-product AI assistants are just reactive chatbots, so they miss the Majority of users who are confused but never click “help.” The real unlock is behavior-aware, proactive guidance that detects struggle, intervenes contextually in the flow, and feeds those friction signals back into product design. Teams that treat assistance as part of the product (and measure its impact on activation, drop-off, and retention) ship far better products than those who just improve documentation.

Most AI walkthrough tools generate long, generic feature tours that users skip, because they ignore user goals, behavior, and activation patterns. Effective tours are short, contextual, and behavior-driven, optimized around a few critical actions that reliably lead to first value. If completion of your tour doesn’t at least 2x activation vs skippers, you have a tooltip generator, not an activation engine.