Prompt Library

ChatGPT Prompts for Product Designers

30 copy-paste prompts

Thirty role-specific prompts that turn ChatGPT into a tireless design partner: from first concept to clean developer handoff. Copy, paste, and swap in your own [PRODUCT], [FEATURE], and [USER].

In short: This page contains 30 copy-paste ready prompts, organized into 6 categories with a description and pro tip for each. The first 5 prompts are free instantly, no signup needed. Drafted with AI writing assistance and checked against our editorial policy.

Louis Corneloup
By Louis Corneloup · Founder, Techpresso
Last updated ·Drafted with AI assistance (how we make prompts)

Concept Ideation

5 prompts

Divergent Concept Generator

1/30

✨ What it does

ChatGPT generates 8 distinct directions for [FEATURE] on [PRODUCT] for [USER], each solving the job differently, with risks and a shortlist. Pick two or three to sketch and drop the lookalikes from your board.

<context> I am a product designer working on [PRODUCT]. I need to explore a new [FEATURE] for [USER]. We are in early divergent ideation and want breadth before we converge. </context> <task> 1. Generate 8 distinct concept directions for [FEATURE], each solving the core job differently (do not just reskin one idea). 2. For each concept give: a one-line name, the core mechanic, the primary user benefit, and the riskiest assumption. 3. Rank the 8 by a 1-5 desirability score and a 1-5 feasibility score. 4. Flag the 2 concepts that are most non-obvious but still viable, and explain why they are worth a sketch. </task>

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Pro tip: After the list, reply 'expand #3 and #7 into rough screen-by-screen flows' to drill into the winners without re-prompting.

Analogous Inspiration Miner

2/30

✨ What it does

ChatGPT names 6 products outside your industry that solve a similar job to [FEATURE] for [USER] on [PRODUCT], and translates each pattern. Steal one pattern and prototype it in your own context.

<context> I am designing [FEATURE] for [PRODUCT], used by [USER]. I want to break out of category clichés by borrowing patterns from unrelated industries. </context> <task> 1. Identify 6 products or experiences from OUTSIDE my industry that solve a structurally similar problem to [FEATURE]. 2. For each, describe the specific interaction pattern or mental model that works. 3. Translate each pattern into a concrete design idea for [PRODUCT]. 4. Note one pitfall to avoid when adapting that pattern to my context. </task>

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Pro tip: Name a beloved app in your prompt ('think Duolingo streaks') to anchor ChatGPT on the emotional mechanic, not just the UI.

Problem Reframing Partner

3/30

✨ What it does

ChatGPT restates the [USER] problem with [PRODUCT] in five different framings and suggests questions to test each one. Pick the framing you will validate and ask those questions before anyone designs.

<context> My team frames the problem as: '[USER] struggles to do X with [PRODUCT].' I suspect we are solving the wrong problem and want to challenge the framing before designing. </context> <task> 1. Restate my problem in 5 alternative framings, each shifting the assumed cause or the desired outcome. 2. For each reframe, state what new solution space it opens. 3. Identify which framing is most likely to be the real root problem and why. 4. Suggest 2 quick research questions to validate the strongest reframe before we commit. </task>

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Pro tip: Paste a raw user-interview quote into the context so ChatGPT reframes against real language, not your internal jargon.

Constraint-Driven Brainstorm

4/30

✨ What it does

ChatGPT generates 6 ideas for [FEATURE] on [PRODUCT] that stay offline, ship in one sprint, add no screens, and work for a non-technical [USER]. You choose the best effort-to-impact idea and design that one.

<context> I am designing [FEATURE] for [PRODUCT] under tight constraints: it must work offline, ship in one sprint, and add zero new screens. [USER] is non-technical. </context> <task> 1. Generate 6 ideas that respect ALL three constraints simultaneously. 2. For each, explain how it honors the offline, one-sprint, and no-new-screen limits. 3. Highlight any idea that turns a constraint into an advantage. 4. Call out the single idea with the best effort-to-impact ratio and justify it. </task>

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Pro tip: List your real constraints explicitly — ChatGPT brainstorms far better when boxed in than when asked to 'be creative.'

Opportunity Sizing Sketch

5/30

✨ What it does

ChatGPT scores [LIST IDEAS] for [PRODUCT] on reach, impact, confidence, and effort, with assumptions stated, and ranks a shortlist for [USER]. You challenge the assumptions, then design the top idea first.

<context> I have a backlog of feature ideas for [PRODUCT]. I need to prioritize which to design first for [USER]. The ideas are: [LIST IDEAS]. </context> <task> 1. Score each idea on reach, impact, confidence, and effort (RICE), using reasonable assumptions you state explicitly. 2. Produce a ranked RICE table. 3. Recommend the top 3 to design next and explain the trade-offs. 4. Flag any idea that looks high-value but is too uncertain to start without research. </task>

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Pro tip: Ask ChatGPT to 'mark every assumption you invented in bold' so you can sanity-check the inputs before trusting the ranking.

Design Briefs

5 prompts

One-Page Design Brief

6/30

✨ What it does

ChatGPT drafts a one-page brief for [FEATURE] on [PRODUCT] for [USER], covering problem, metrics, scope, and a testable hypothesis. Circulate it to PM, eng, and marketing and lock the open questions on your side.

<context> I am kicking off design for [FEATURE] on [PRODUCT], serving [USER]. Stakeholders are PM, eng lead, and marketing. I need a crisp brief to align everyone. </context> <task> 1. Draft a one-page design brief with these sections: Problem, Target user, Success metrics, Scope, Out of scope, Constraints, Open questions. 2. Keep each section to 2-4 bullet points, written for a mixed audience. 3. Phrase success metrics as measurable outcomes, not features. 4. End with a single sentence stating the design hypothesis we are testing. </task>

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Pro tip: Feed ChatGPT the meeting notes from kickoff and ask it to 'fill the brief only from what is stated, leave gaps as open questions.'

Success Metrics Definer

7/30

✨ What it does

ChatGPT turns the vague engagement goal into 4-6 measurable metrics for [FEATURE] on [PRODUCT] for [USER], including a main number and a guardrail. Pick the metrics you will report and add tracking for them.

<context> We are designing [FEATURE] for [PRODUCT]. Leadership wants to know how we will measure success for [USER], but we only have a vague goal: 'improve engagement.' </context> <task> 1. Translate 'improve engagement' into 4-6 specific, measurable metrics tied to [FEATURE]. 2. For each metric, give a definition, a data source, and a sensible target direction. 3. Separate leading indicators from lagging indicators. 4. Recommend the single North Star metric and one guardrail metric to prevent gaming it. </task>

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Pro tip: Ask for the guardrail metric explicitly — ChatGPT will otherwise optimize the brief toward a single number that is easy to game.

Stakeholder Alignment Map

8/30

✨ What it does

ChatGPT maps each role in [LIST ROLES] to a likely priority and concern about [FEATURE] on [PRODUCT], and names the two likeliest conflicts. Ask the pre-empting questions in your briefing before those conflicts land.

<context> I am about to brief [FEATURE] for [PRODUCT]. The stakeholders are [LIST ROLES]. They have competing priorities and I want to anticipate friction. </context> <task> 1. For each stakeholder role, infer their likely top priority and their biggest concern about [FEATURE]. 2. Identify the 2 most likely points of conflict between stakeholders. 3. Suggest framing for the brief that pre-empts each conflict. 4. Draft one alignment question to ask each stakeholder before design starts. </task>

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Pro tip: Name real people and their past objections in the context; ChatGPT tailors the framing far better with specifics than with generic roles.

Scope-Cutting Advisor

9/30

✨ What it does

ChatGPT splits the bloated [FEATURE] brief on [PRODUCT] into must, should, and could, and defines the smallest version that still helps [USER]. Ship that thin version this quarter and phase the rest of your brief.

<context> The brief for [FEATURE] on [PRODUCT] has grown too big to ship this quarter. I need to cut scope without losing the core value for [USER]. </context> <task> 1. Separate the brief into must-have, should-have, and could-have using MoSCoW. 2. Define the smallest version that still delivers the core user value (the walking skeleton). 3. List what we defer and the explicit risk of deferring each item. 4. Propose a phased rollout: v1 now, v1.1 next, with the trigger for each phase. </task>

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Pro tip: Paste the full brief and tell ChatGPT 'be ruthless — assume we have half the time we think' to get a genuinely lean v1.

Competitive Brief Audit

10/30

✨ What it does

ChatGPT audits how [LIST COMPETITORS] handle the job of [FEATURE] for [USER] on [PRODUCT], naming table stakes, gaps, and a different angle. Pick the gap you can own and write it into your brief.

<context> Before designing [FEATURE] for [PRODUCT], I want to ground the brief in how competitors handle the same job for [USER]. Key competitors: [LIST COMPETITORS]. </context> <task> 1. For each competitor, summarize how they solve the job [FEATURE] addresses (use general knowledge, flag uncertainty). 2. Identify the table-stakes elements we must match. 3. Identify 2 gaps or weaknesses we could exploit as differentiation. 4. Recommend one positioning angle for the brief and the design decision it implies. </task>

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Pro tip: Treat competitor facts as hypotheses — ask ChatGPT to label anything it is unsure of so you verify before writing it into the brief.

User Flows

5 prompts

End-to-End Flow Outliner

11/30

✨ What it does

ChatGPT lists a numbered happy path for [USER] completing [FEATURE] in [PRODUCT], with branches and the highest drop-off risk. Sketch those screens in order and design the risky step first in your file.

<context> I am mapping the flow for [USER] completing the core task of [FEATURE] in [PRODUCT]. I want a complete happy-path flow before I touch Figma. </context> <task> 1. List every step from entry point to task completion as a numbered sequence. 2. For each step, note the user goal, the screen or surface, and the primary action. 3. Mark decision points where the flow branches. 4. Identify the single step most likely to cause drop-off and why. </task>

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Pro tip: Once the happy path is set, ask 'now add the unhappy paths for each decision point' to expand without losing the structure.

Edge-Case Enumerator

12/30

✨ What it does

ChatGPT enumerates empty, error, permission, network, and extreme-input cases for [FEATURE] in [PRODUCT] for [USER], with behavior and microcopy. You decide which cases ship in v1 and spec the rest as later work.

<context> I have designed the happy path for [FEATURE] in [PRODUCT] for [USER]. Before handoff I need to cover edge cases and error states. </context> <task> 1. Enumerate edge cases across these categories: empty states, error states, permission/auth, network failure, and extreme inputs. 2. For each edge case, describe the trigger and the ideal user-facing behavior. 3. Recommend the message or microcopy the user should see. 4. Prioritize which edge cases must be designed for v1 versus deferred. </task>

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Pro tip: Ask ChatGPT to 'write the error microcopy in our voice' and paste a sample of your product copy so the states sound on-brand.

User Journey Mapper

13/30

✨ What it does

ChatGPT maps [USER] adopting [FEATURE] in [PRODUCT] across awareness, first use, activation, habit, and advocacy, with emotion and a design move per stage. You pick one stage to improve and design that intervention.

<context> I want a journey map for [USER] adopting [FEATURE] in [PRODUCT], spanning awareness to habitual use, not just a single session. </context> <task> 1. Break the journey into stages: Awareness, First use, Activation, Habit, Advocacy. 2. For each stage describe the user goal, emotional state, and key touchpoint. 3. Identify the biggest friction or doubt at each stage. 4. Suggest one design intervention per stage to move the user forward. </task>

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Pro tip: Add your real activation metric to the context so the 'Activation' stage maps to the moment your data actually tracks.

Information Architecture Sorter

14/30

✨ What it does

ChatGPT reorganizes [LIST SECTIONS] in [PRODUCT] around how [USER] thinks about [FEATURE], as a three-level labeled hierarchy. Test the labels with a few users, then update your nav.

<context> I am restructuring the navigation of [PRODUCT] to better support [FEATURE] for [USER]. Current sections are: [LIST SECTIONS]. </context> <task> 1. Propose a cleaner IA, grouping content by user mental model rather than internal team structure. 2. Show the proposed nav as a labeled hierarchy (max 3 levels deep). 3. Explain each major grouping decision in one sentence. 4. Flag any current label that is jargon and suggest a clearer user-facing term. </task>

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Pro tip: Ask for a quick 'card-sort rationale' so you can defend the new IA to stakeholders who are attached to the old structure.

Onboarding Flow Designer

15/30

✨ What it does

ChatGPT designs a short onboarding path for new [USER] signups on [PRODUCT] that reaches the aha moment of [FEATURE] with as few steps as possible. Prototype that path and cut any step that delays first value for you.

<context> New [USER] sign up for [PRODUCT] but drop off before experiencing the value of [FEATURE]. I need an onboarding flow that gets them to first value fast. </context> <task> 1. Define the single 'aha moment' for [FEATURE] and the shortest path to it. 2. Design a step-by-step onboarding flow with the minimum steps needed to reach that moment. 3. For each step, specify the goal, what to show, and what to defer. 4. Recommend where to use progressive disclosure versus required input. </task>

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Pro tip: Tell ChatGPT 'cut any step that is not strictly required to reach the aha moment' — it defaults to over-onboarding otherwise.

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Prototyping Specs

5 prompts

Interaction Spec Writer

16/30

✨ What it does

ChatGPT writes a state-by-state interaction spec for [DESCRIBE COMPONENT] in [FEATURE] on [PRODUCT], including transitions and accessibility. You build the prototype from those states and check focus and disabled behavior.

<context> I am specifying the interactions for [FEATURE] in [PRODUCT] so I can build a high-fidelity prototype. The key component is [DESCRIBE COMPONENT]. </context> <task> 1. Write a detailed interaction spec for the component: default, hover, active, focus, disabled, loading, and error states. 2. For each state, describe the visual change and the trigger. 3. Specify transition timing and easing recommendations. 4. Note any accessibility behavior (keyboard, screen reader, focus order) for each state. </task>

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Pro tip: Ask ChatGPT to output the states as a table so you can paste it straight into your Figma prototype documentation frame.

Microinteraction Designer

17/30

✨ What it does

ChatGPT proposes 3 purposeful microinteractions for [DESCRIBE ACTION] in [FEATURE] on [PRODUCT] for [USER], with feedback and timing. Pick the one that fits the context and add it in your prototype.

<context> I want to add a delightful but purposeful microinteraction to [FEATURE] in [PRODUCT] for [USER]. The trigger is [DESCRIBE ACTION]. </context> <task> 1. Propose 3 microinteraction concepts for this trigger, each with a clear functional purpose (not decoration). 2. For each, describe the feedback, the animation, and the duration. 3. Explain the user benefit and the risk of overdoing it. 4. Recommend the one most appropriate for our context and why. </task>

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Pro tip: Specify your product’s personality ('calm and professional' vs 'playful') so the animation suggestions match your brand’s motion language.

Component Variant Spec

18/30

✨ What it does

ChatGPT defines properties and a full variant matrix for [COMPONENT] in the [PRODUCT] system used by [FEATURE], including invalid combos and defaults. Build only the valid variants in your Figma file.

<context> I am building a [COMPONENT] for the [PRODUCT] design system, used across [FEATURE]. I need to define its full variant matrix before building in Figma. </context> <task> 1. Define the relevant properties for this component (e.g. size, variant, state, icon). 2. Produce the full variant matrix as a table of property combinations. 3. Flag combinations that are invalid or should be blocked. 4. Recommend sensible defaults for each property. </task>

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Pro tip: Ask ChatGPT to name the variants using your design-system convention so they map 1:1 to Figma component properties.

Prototype Test Script

19/30

✨ What it does

ChatGPT writes an unmoderated test plan for the [FEATURE] prototype in [PRODUCT], with 3-4 goal-based tasks and success criteria for [USER]. Recruit five participants and run the tasks without hinting at your UI.

<context> I have a clickable prototype of [FEATURE] in [PRODUCT] and need to run unmoderated usability tests with 5 [USER] participants. </context> <task> 1. Write a test plan with a clear objective and 3-4 realistic tasks for [FEATURE]. 2. Phrase each task as a goal scenario, not as instructions that reveal the UI. 3. Add a short pre-test screener and 3 post-task questions. 4. Define the success criteria and what observations would invalidate the design. </task>

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Pro tip: Ask ChatGPT to rewrite any task that 'leaks the answer' — leading tasks are the most common reason usability results mislead.

Responsive Behavior Spec

20/30

✨ What it does

ChatGPT specs how the [FEATURE] screen in [PRODUCT] reflows for [USER] on mobile, tablet, and desktop, including touch targets and fallbacks. Prototype each breakpoint and fix anything that collapses in your layout.

<context> The [FEATURE] screen in [PRODUCT] must work across mobile, tablet, and desktop for [USER]. I need to spec the responsive behavior before prototyping. </context> <task> 1. For each breakpoint (mobile, tablet, desktop), describe the layout and what changes from the previous size. 2. Specify which elements reflow, stack, hide, or collapse into menus. 3. Define touch-target and spacing rules for the smallest breakpoint. 4. Flag the single element most at risk of breaking responsively and propose a fallback. </task>

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Pro tip: Give ChatGPT your actual breakpoint pixel values so the reflow advice maps to your real grid instead of generic ranges.

Design Critique

5 prompts

Heuristic Evaluation

21/30

✨ What it does

ChatGPT evaluates [DESCRIBE DESIGN] for [FEATURE] in [PRODUCT] against Nielsen's ten heuristics, with severity and a concrete fix each. Fix the high-severity violations before your next critique.

<context> I want a structured critique of my [FEATURE] design in [PRODUCT] for [USER]. I will describe the screens and key interactions below: [DESCRIBE DESIGN]. </context> <task> 1. Evaluate the design against Nielsen's 10 usability heuristics. 2. For each heuristic, state whether it is met, partially met, or violated, with a specific reason. 3. Rank the violations by severity (cosmetic, minor, major, catastrophic). 4. Give one concrete fix for each major or catastrophic issue. </task>

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Pro tip: Describe the design in plain words OR paste a screenshot if using GPT-4o vision — ChatGPT critiques visuals directly when you attach the image.

Devil's Advocate Reviewer

22/30

✨ What it does

ChatGPT argues against your [FEATURE] direction in [PRODUCT] from [DESCRIBE], names three shaky assumptions, and the change that would reduce risk most. Test that assumption or make the change before you present.

<context> I am attached to my current direction for [FEATURE] in [PRODUCT] and want someone to poke holes before I present it. Here is the design and my rationale: [DESCRIBE]. </context> <task> 1. Argue the strongest case AGAINST this design direction. 2. Identify the 3 assumptions it relies on that could be wrong. 3. Describe a realistic scenario where [USER] would be frustrated or confused. 4. Suggest the single change that would most de-risk the design. </task>

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Pro tip: Tell ChatGPT 'do not soften the critique' — it tends toward politeness, and the harsh version is what saves you in the review.

Accessibility Critique

23/30

✨ What it does

ChatGPT audits [DESCRIBE DESIGN AND COLORS] for [FEATURE] in [PRODUCT] against WCAG 2.2 AA and lists likely violations with fixes. Correct contrast, focus, and targets, then recheck before your handoff.

<context> I need to audit my [FEATURE] design in [PRODUCT] for accessibility before handoff. The design is described here: [DESCRIBE DESIGN AND COLORS]. </context> <task> 1. Check the design against WCAG 2.2 AA for color contrast, focus, target size, and text alternatives. 2. List each likely violation with the specific guideline number. 3. Recommend a fix for each, with concrete values where relevant (e.g. contrast ratio targets). 4. Flag anything that needs real assistive-tech testing rather than a desk review. </task>

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Pro tip: Paste your hex color pairs into the context so ChatGPT can estimate contrast ratios instead of giving you generic advice.

Copy and Microcopy Critique

24/30

✨ What it does

ChatGPT critiques and rewrites each string in [PASTE COPY] for [FEATURE] in [PRODUCT] so [USER] gets clear, action-oriented language. Paste the keepers into your file and drop any jargon that survived.

<context> I want to tighten the UX writing in [FEATURE] of [PRODUCT] for [USER]. Here is all the current copy: [PASTE COPY]. </context> <task> 1. Critique each string for clarity, length, tone, and action-orientation. 2. Rewrite each weak string, keeping our voice consistent. 3. Flag any jargon, hedging, or vague CTAs and replace them. 4. Ensure button labels describe the outcome, not the mechanism. </task>

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Pro tip: Provide 2-3 examples of your existing on-brand copy first so ChatGPT matches voice instead of defaulting to generic SaaS tone.

Design Decision Defender

25/30

✨ What it does

ChatGPT steelmans [OBJECTION] to [DECISION] on [FEATURE] in [PRODUCT], then drafts a calm, evidence-based reply. Use that reply in your meeting and stay with the user need.

<context> A stakeholder is pushing back on a design decision I made for [FEATURE] in [PRODUCT]. My decision: [DECISION]. Their objection: [OBJECTION]. </context> <task> 1. Steelman the stakeholder's objection so I fully understand it. 2. Lay out the strongest design rationale for my decision, grounded in user needs and principles. 3. Identify any valid point in their objection I should incorporate. 4. Draft a calm, evidence-based response I can say in the meeting. </task>

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Pro tip: Ask for the response in spoken language, not written — ChatGPT defaults to memo prose that sounds stilted said aloud.

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Developer Handoff

5 prompts

Handoff Spec Generator

26/30

✨ What it does

ChatGPT generates a handoff spec for [FEATURE] of [PRODUCT] covering flow, states, components, edge cases, and given/when/then criteria. Add the remaining open questions, then send it to your engineering partners.

<context> I am handing off [FEATURE] of [PRODUCT] to engineering. I need a complete spec document so developers can build without back-and-forth. </context> <task> 1. Generate a handoff spec with sections: Overview, User flow, Screens and states, Component list, Interaction behavior, Edge cases, and Acceptance criteria. 2. Write acceptance criteria in clear given/when/then format. 3. Note any dependencies, API needs, or open questions for eng. 4. Keep it scannable with headings and bullets, not walls of text. </task>

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Pro tip: Ask ChatGPT to format the output in Markdown so you can paste it directly into Notion, Linear, or your ticketing tool.

Design Token Documenter

27/30

✨ What it does

ChatGPT turns [PASTE COLORS, SPACING, TYPE] into semantic tokens for [FEATURE] of [PRODUCT] and flags inconsistent values. Rename tokens in your file and ask eng to use only those names.

<context> I need to document the design tokens used in [FEATURE] of [PRODUCT] so engineering applies them consistently. My values are: [PASTE COLORS, SPACING, TYPE]. </context> <task> 1. Organize my raw values into a token structure: color, spacing, typography, radius, shadow. 2. Propose semantic token names (e.g. color-surface-primary) mapped to my raw values. 3. Flag any inconsistent or one-off values that should be normalized. 4. Output the tokens in a clean table ready for the design system doc. </task>

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Pro tip: Ask for the tokens as a JSON object too — many design-to-code tools import token JSON directly, saving manual entry.

Acceptance Criteria Writer

28/30

✨ What it does

ChatGPT writes given/when/then acceptance criteria for every [FEATURE] screen in [PRODUCT] that matters to [USER], including errors and empty states. Review them with QA, then treat that list as your definition of done.

<context> The [FEATURE] design in [PRODUCT] is ready and I need precise acceptance criteria so QA and eng know exactly what 'done' means for [USER]. </context> <task> 1. Write acceptance criteria for every screen and key interaction in given/when/then format. 2. Include criteria for edge cases, error states, and empty states. 3. Add accessibility acceptance criteria (keyboard, focus, contrast). 4. Flag any criterion that is ambiguous and needs a product decision before build. </task>

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Pro tip: Tell ChatGPT to 'mark any criterion you could not fully specify from my input' so gaps surface before QA finds them.

Eng Question Pre-empter

29/30

✨ What it does

ChatGPT lists the ten questions frontend will ask about [FEATURE] of [PRODUCT], grouped by data, states, interaction, and responsive behavior. Answer them in your spec so the handoff does not stall.

<context> Before I hand off [FEATURE] of [PRODUCT] to engineering, I want to anticipate the questions developers will ask so I can answer them in the spec. </context> <task> 1. List the 10 most likely questions a frontend developer would ask about this design. 2. Group them by theme: data, states, interaction, responsive, and edge cases. 3. For each, note what I should add to the spec to pre-empt it. 4. Highlight the 3 questions that, if unanswered, would most likely block the build. </task>

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Pro tip: Paste your draft spec and ask ChatGPT 'which of these 10 are already answered?' so you only fill the genuine gaps.

Implementation Review Checklist

30/30

✨ What it does

ChatGPT produces a yes/no design QA checklist for the built [FEATURE] in [PRODUCT] for [USER], with severity labels. You walk the live build, log fails, and block release on the serious ones.

<context> Engineering has built [FEATURE] for [PRODUCT] from my designs. I need to do a design QA review of the implementation against the spec for [USER]. </context> <task> 1. Generate a design QA checklist covering visual fidelity, spacing, typography, states, interactions, and responsive behavior. 2. Add a section for accessibility checks and one for motion/animation timing. 3. Phrase each item as a yes/no check I can run quickly against the live build. 4. Recommend a severity label for each item so I can triage what blocks release. </task>

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Pro tip: Ask ChatGPT to order the checklist by where bugs most often hide — states and responsive edges — so your review catches the worst first.

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Frequently Asked Questions

Copy any prompt, then replace the bracketed placeholders — [PRODUCT], [FEATURE], and [USER] — with your real project details. Each prompt uses a context-and-task structure so ChatGPT understands your situation before generating output. The more specific you are in the context block, the sharper and more usable the result. Paste real notes, quotes, or constraints wherever possible.
GPT-4o handles most of these prompts well and supports image uploads, which is invaluable for the design critique and accessibility prompts — you can attach a screenshot and get a direct visual review. For long handoff specs and structured reasoning, the reasoning models produce more thorough, better-organized output. Free-tier models work for ideation but tend to be shallower on critique and edge cases.
No. ChatGPT is excellent for generating hypotheses, drafting test scripts, enumerating edge cases, and pressure-testing your thinking — but it cannot observe real users. Treat its output as a fast, informed first draft that you validate with actual research. The prototype test script and journey mapping prompts are designed to feed into real testing, not replace it.
Only if you accept the first output as final. These prompts are built to expand your thinking — generating divergent concepts, cross-industry analogies, and devil's-advocate critiques — not to hand you a finished design. The judgment, taste, and synthesis remain yours. Use ChatGPT to go wider and catch blind spots, then apply your own craft to converge.
Front-load the context block with specifics: your actual constraints, real user quotes, your brand voice, hex values, and breakpoints. Iterate in the same conversation rather than restarting — ask ChatGPT to expand, critique, or reformat its own output. And explicitly ask it to flag assumptions or gaps, since the most useful design feedback is often about what it could not infer from your input.

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