Mark each AI-interaction state as defined or missing
For one AI feature, mark whether generating, thinking/reasoning-visible, retrying, streaming, failed, rate-limited, and uncertain/low-confidence states are designed.
AI-STATE COVERAGE SCORE
AIStateKit
Enter which standard AI-interaction states your feature has designed for and get a state-coverage completeness score for AI-specific UX.
- Uses your inputs in a transparent calculation
- Instant result with practical next steps
- No signup required
Planning guidance only. Validate important decisions with customer evidence and your delivery team.
YOUR INPUTS
Build your result
Complete every field. The result updates only when you choose Calculate.
How it works
We calculate an AI-state-coverage score
Your score is the share of the seven standard AI-interaction states that are defined, out of 100.
Get the specific states to design next
Missing states are listed by name, with rate-limited and uncertain states flagged as the most commonly skipped in practice.
Continue learning: Designing AI prompts for MVP features · AI tools in the MVP development workflow
Frequently asked questions
Does AIStateKit generate the actual UI for these states?
No. AIStateKit computes a completeness score entirely from the yes/no answers you give — it does not generate visual designs and does not run a real AI/LLM generation step. It is a coverage checklist for AI-specific interaction states.
How is AIStateKit different from State Craft?
State Craft scores the generic UI-state set that applies to almost any component — loading, empty, success, warning, disabled, error. AIStateKit scores a different, AI-specific set of states that only apply to features built around a generative AI response: generating, thinking/reasoning-visible, retrying, streaming, failed, rate-limited, and uncertain/low-confidence. An AI feature typically needs both State Craft's generic set for its surrounding UI and AIStateKit's set for the AI response itself.
What does a "thinking/reasoning-visible" state look like?
Any UI that shows the AI is working through a multi-step process before a final answer — a visible chain-of-reasoning summary, an intermediate status message, or a step-by-step progress indicator, as opposed to a plain spinner.
Why does "uncertain/low-confidence" matter for AI features?
Generative AI features can produce a plausible-looking but wrong or low-confidence answer. A dedicated uncertain state — a confidence indicator, a caveat, or a suggestion to verify — helps users calibrate trust instead of treating every output as equally reliable.
What should I do with a low coverage score?
Prioritize the failed and rate-limited states first if they are missing — both are common in production and, left undesigned, tend to surface as a raw error or a frozen UI to real users.
How We Compare
| Feature | MVPHub | Figma | Storybook |
|---|---|---|---|
| AI-interaction-specific state coverage score | Included | Not included | Not included |
| Named list of missing AI states | Included | Not included | Not included |
| Design and prototype the states visually | Not included | Included | Limited |
| Live component development environment | Not included | Not included | Included |
Figma is where each AI-interaction state actually gets designed, and Storybook is where built components get developed and previewed state by state. AIStateKit runs earlier — a focused checklist confirming which AI-specific states your feature needs before either tool comes into play.
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