Prompt

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UX Critique — First 90 Seconds

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

  • Role-Based
  • Chain-of-Thought
  • Output-Format
Download .mdOpen in Studio~217 words
**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 it

  1. Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer. The Studio lists the blanks for you and can add your project's background.
  2. Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
  3. Not sure what it produces? Give it a test run in the Studio first, then refine with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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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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Works with

Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.

New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.

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