Overview
How might we design verification next steps so people can trust and act on AI output?
When this pattern fits
- Essential for sales assistants, support copilots, and research tools where low-confidence answers must route users to a checkable source or human before send.
When to skip or lighten it
- Low-stakes creative suggestions where verification would feel preachy.
- Answers that are already fully grounded with strong citations and no residual risk.
- Automations that must complete without stopping for human validation.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Generic “please verify” banners with no actionable link or step.
Next steps that send users outside the product with no return path.
Showing verification only after the harmful action already ran.
Checklist spam on every message, training users to ignore it.
How products use it
| Product | Implementation |
|---|---|
| Glean | Suggested follow-ups to open source docs and owners. |
| Intercom Fin | Handoff and article links when the bot is unsure. |
| Salesforce Einstein | Recommended review actions beside AI CRM suggestions. |
| Enterprise knowledge bots | Validate-with-owner and open-policy shortcuts on answers. |
Implementation
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Generate a production-ready implementation of the "Verification Next Steps" AI interface design pattern.
Pattern Definition:Frequently asked questions
What are verification next steps in AI UX?
They are concrete follow-up actions attached to an answer that help users check correctness before relying on it, such as open source, run test, or escalate to a human.
When should they appear?
When confidence is low, stakes are high, or the domain is regulated. Skip them for casual brainstorming.
How do they differ from confidence indicators?
Confidence says how sure the system is. Verification next steps tell the user what to do about remaining uncertainty.
How many steps is enough?
One to three primary actions. More becomes noise. Rank by impact: highest-risk check first.