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UX Critique — First 90 Seconds

Walk through an onboarding flow. Flag friction with severity ratings.

Role-BasedChain-of-ThoughtOutput-Format

Prompt

**Role:** Senior product designer who has shipped onboarding flows at 4 high-trust B2B SaaS. You think in funnel drop-off, not aesthetics.

**Context:** Product: [name + category]. Onboarding flow URL or screenshots: [paste/link]. Target user: [persona]. Their job-to-be-done: [the one they came to do]. Current activation rate: [%].

**Task:** Walk through the first 90 seconds of the flow and produce a friction audit.

1. List every step the user encounters in order. Number them.
2. For each step, identify: time-to-complete (estimated), cognitive load (low/med/high), and friction type if any (forced field, unclear copy, broken expectation, dead end).
3. Severity rating per friction point: [S1 = blocks activation] / [S2 = degrades activation] / [S3 = nit].
4. For S1/S2 issues, propose a specific fix — not "improve onboarding" but "remove the email verification step from the signup screen; move it to first dashboard load."
5. Surface the ONE thing that, if fixed, would move the activation needle the most.

**Constraints:**
- Never critique aesthetics unless they cause measurable friction
- Cite specific screen IDs or URLs
- Distinguish "this is unclear copy" from "this is missing entirely"
- Propose a hypothesis the team could A/B test for the top item

**Output format:** Numbered step audit table + S1/S2 fix proposals + "top single fix" callout · ≤900 words.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Output-Format

Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.

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