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AI in your workflow

Stage by stage: where AI is worth using, what to give it, what to check afterwards, and what stays with you.

Craft guidance
  1. Research
  2. AI synthesis
  3. Designer validation
  4. Flow
  5. Prototype
  6. AI critique
  7. Designer decision
One way AI fits into product design. The highlighted steps are yours: the model proposes, and a designer checks and decides.

Discovery

When to use AI. You are framing a problem and planning how to learn about it.

Good use

"List the assumptions my research plan depends on, and which questions my method cannot answer."

Weak use

"Tell me what users of this product want."

What to give the AI

  • The decision the research should inform
  • Your draft plan and questions
  • What you already know, and how you know it

What to review afterwards

  • Whether flagged assumptions are real
  • Whether suggested methods fit your access to participants
  • Any competitor facts, against the competitor's own site

What the designer still owns

  • Defining the problem
  • Choosing who to talk to
  • Deciding what counts as evidence

Common failure

Treating a model's description of users as research. It is a summary of what is commonly written, not of your users.

Example prompt: Pressure-test a research planShow

Prompt

I am planning research for [product and the decision this research should inform].

My current plan:
[paste plan: goals, method, participants, questions]

Before suggesting improvements:
1. List the assumptions my plan depends on.
2. For each research question, say whether my method can actually answer it, and why.
3. Point out any question that is leading, double-barrelled or asks people to predict their own behaviour.

Then suggest changes, keeping to my timeline of [timeline] and [number] participants.

Do not write findings or guess what participants will say.
Why this prompt works, and what to verify

Research

When to use AI. You have raw notes or transcripts and need a first pass at structure.

Good use

"Group these notes into candidate themes, with participant IDs and exact quotes for each."

Weak use

"Summarise how participants felt."

AI must not invent participant sentiment. Check your organisation's rules before putting participant data into any AI tool.

What to give the AI

  • Notes with a participant ID on each
  • The research questions
  • Consent to process the data in that tool

What to review afterwards

  • Every quote against the source
  • The count of participants behind each theme
  • Notes left unsorted

What the designer still owns

  • Validating themes against the source material
  • Judging what matters
  • Protecting participant data

Common failure

Invented sentiment. A model will say participants were frustrated when the notes only say what they did.

Example prompt: Find candidate themes in interview notesShow

Prompt

Below are notes from [number] user interviews about [topic]. Each note starts with a participant ID.

[paste notes]

Group the notes into candidate themes.

For each theme give:
- a neutral name
- the participant IDs that support it
- two or three direct quotes, copied exactly
- any participant who contradicts it

Rules:
- Use only what is in the notes. Do not add sentiment, motivation or frequency that is not stated.
- If a theme rests on one participant, label it "single source".
- List notes that fit no theme under "Unsorted".
Why this prompt works, and what to verify

Information architecture

When to use AI. You have a draft structure and want it challenged before testing with people.

Good use

"For each of these tasks, which top-level item would a new user open, and where are two items plausible?"

Weak use

"Create the sitemap for my product."

What to give the AI

  • The structure as an indented list
  • Who the users are
  • Their top tasks

What to review afterwards

  • Whether predicted paths match a real tree test
  • Labels flagged as jargon, with someone outside the team

What the designer still owns

  • The mental model you are designing for
  • Final names and groupings
  • Testing with real users

Common failure

A tidy, symmetrical structure that matches no one's mental model.

Example prompt: Challenge a navigation structureShow

Prompt

Here is the proposed navigation for [product], used by [who].

[paste the structure as an indented list]

Their top tasks are:
[list 5 to 8 tasks]

For each task, say which top-level item a first-time user would most likely open, and how confident you are. Flag any task where two items are plausible.

Then list:
- labels that are jargon or internal names
- categories that overlap
- items a user might expect that are missing

Do not propose a new structure yet.
Why this prompt works, and what to verify

User flows

When to use AI. The happy path is drawn and you need to find what you missed.

Good use

"Identify edge cases I may have missed at each step."

Weak use

"Design the entire product flow for me."

What to give the AI

  • Numbered steps
  • The user and their goal
  • Fixed constraints: platform, permissions, sign-in

What to review afterwards

  • Cases your constraints make impossible
  • Priorities, with product and engineering

What the designer still owns

  • Understanding user needs
  • Prioritisation
  • Product constraints
  • Final flow decisions

Common failure

Accepting an added step for every edge case, until the flow is longer than the problem.

Example prompt: Find the edge cases in a flowShow

Prompt

Here is a user flow for [task] in [product], for [user].

[paste the steps, numbered]

Constraints: [platform, sign-in state, permissions, anything fixed]

For each step, list what could go wrong or differ:
- missing, invalid or unusual input
- the user leaving and returning
- slow or failed network
- permissions and roles
- empty, very large or very long data
- going back or repeating the step

Mark each as "must handle", "should handle" or "rare". Do not redesign the flow.
Why this prompt works, and what to verify

Wireframes

When to use AI. You want to check content and structure before drawing.

Good use

"List the information this page needs, in priority order, and the states it must handle."

Weak use

"Generate the wireframe."

What to give the AI

  • The page and its purpose
  • Where the user comes from and what they do next

What to review afterwards

  • Whether the priority order is right for your users
  • States against real data conditions

What the designer still owns

  • What the page is for
  • Layout decisions
  • What to leave out

Common failure

Treating a generated wireframe as a design. It is a typical page, not your page.

Example prompt: Check what a page must containShow

Prompt

I am wireframing the [page name] page of [product].

The user arrives from [where] wanting to [goal].
After this page they should be able to [next step].

List:
1. The information the user needs to decide or act, in priority order.
2. The actions available, primary first.
3. The states this page needs: empty, loading, error, partial, no permission.
4. Content that is commonly added to pages like this but is not needed here.

Describe content and priority only. Do not describe layout, components or visual style.
Why this prompt works, and what to verify

UI design

When to use AI. You have a design and want critique, or want variations inside your system.

Good use

"Review this screen. Type every finding as standard, design-system, convention, craft or preference."

Weak use

"Make this look more modern."

What to give the AI

  • The screen
  • Product type, user and task
  • Your design system's tokens and components

What to review afterwards

  • Any standard it cites
  • Contrast and sizes, measured
  • Values that are not in your system

What the designer still owns

  • Visual direction
  • Which critique to act on
  • Final design decisions

Common failure

Generic generated UI: cards everywhere, large radii, a gradient. See the AI-look signals.

Example prompt: Review my designShow

Prompt

Review this interface as a senior product designer.

Context:
- Product type: [e.g. enterprise dashboard]
- User and task: [who, doing what]
- Platform and viewport: [e.g. web, 1440 px]
- Design system: [name, or "custom"]

Do not redesign it immediately.

First evaluate: information hierarchy, alignment, spacing, typography, colour, accessibility, interaction states, information density, consistency and responsive behaviour.

Give every finding one type:
1. Standard requirement (name the standard and criterion)
2. Design-system issue (name the rule)
3. Industry convention
4. Craft guidance
5. Subjective preference

For every finding explain what you noticed, why it matters, its severity, and what I could try.

Where you cannot tell from the image, such as exact contrast or pixel values, say "verify" and tell me what to measure.

Do not invent WCAG requirements, design-system rules or research findings. If you are unsure whether something is a rule, say so.
Why this prompt works, and what to verify

UX writing

When to use AI. You need consistent labels, errors and empty states across a product.

Good use

"Write one error message per validation rule, saying what is wrong and how to fix it."

Weak use

"Write friendly copy for my app."

What to give the AI

  • The real validation rules and states
  • Voice and length limits
  • Existing copy to match

What to review afterwards

  • Each message against what the system actually checks
  • Tone, read aloud
  • Length in your longest supported language

What the designer still owns

  • Voice
  • Whether the message is true
  • Terminology across the product

Common failure

Copy that sounds fine and describes behaviour the system does not have.

Example prompt: Write error messages for a formShow

Prompt

Write error messages for this form in [product].

Fields and validation rules:
[field: rule, for each]

Voice: [e.g. plain, direct, no apology]

For each rule give one message that:
- says what is wrong
- says how to fix it
- uses the field's own label
- is under [number] words

Avoid "invalid", "illegal", "oops" and blaming the user. Give the same message for the inline error and the summary.

If a rule is ambiguous, ask me instead of guessing.
Why this prompt works, and what to verify

Accessibility

When to use AI. You want to catch likely issues during design, before a proper test.

Good use

"List likely WCAG 2.2 AA failures in this design, and separately what cannot be judged from a static image."

Weak use

"Is this accessible?"

An AI accessibility review does not replace accessibility testing. It cannot operate a screen reader, a keyboard or a switch.

What to give the AI

  • The design
  • Platform
  • The standard and level you are held to

What to review afterwards

  • Every cited criterion, in WCAG itself
  • Contrast pairs, measured
  • Everything on the must-test list, with real assistive technology

What the designer still owns

  • Meeting the standard
  • Testing with assistive technology
  • Involving disabled users

Common failure

Unsupported claims: invented criteria, or "passes AA" based on a guess at contrast.

Example prompt: First-pass accessibility reviewShow

Prompt

Do a first-pass accessibility review of this design against WCAG 2.2 Level AA.

Design: [attach or describe]
Platform: [web / iOS / Android]

Report in three groups:
A. Likely failures visible in the design. Cite the success criterion number and name.
B. Things that cannot be judged from a static design and must be tested: keyboard order, focus management, screen reader output, zoom and reflow.
C. Questions for the designer, where intent is unclear.

For contrast, do not estimate ratios from the image. List the text and background pairs I should measure.

This is not a conformance audit. Do not invent WCAG requirements, design-system rules or research findings. If you are unsure whether something is a rule, say so.
Why this prompt works, and what to verify

Prototyping

When to use AI. You need to feel an interaction or test a flow that static frames cannot show.

Good use

"Build this one interaction with these states, so I can test it."

Weak use

"Build my app."

What to give the AI

  • The interaction and its states, in plain language
  • Your tokens and components
  • What you want to learn from the prototype

What to review afterwards

  • Whether it follows your system
  • Keyboard operation
  • Behaviour at small widths

What the designer still owns

  • What the prototype is for
  • What you conclude from it
  • Being clear it is not production code

Common failure

A convincing prototype that quietly introduced its own colours, spacing and components.

Example prompt: Generate UI that follows my design systemShow

Prompt

You are working inside an existing design system. Follow it; do not extend it.

Tokens:
[paste spacing, radius, colour and type tokens]

Components available:
[list components and their variants]

Task: [what to design or build]

Rules:
- Use only the tokens and components above.
- If the task needs something the system lacks, stop and tell me what is missing. Do not invent a colour, radius, spacing value or component.
- After the output, list every token and component you used.
Why this prompt works, and what to verify

Design QA

When to use AI. The design is nearly done and you want a systematic last pass.

Good use

"List the states missing from each component, and what triggers each."

Weak use

"Check my design."

What to give the AI

  • An inventory of components and screens
  • The states you have designed
  • Your tokens

What to review afterwards

  • States that do not apply to your product
  • Reported inconsistencies, in the file itself

What the designer still owns

  • Deciding which inconsistencies are intentional
  • The standard the work is held to

Common failure

Trusting pixel measurements read from a screenshot. Models estimate spacing and size poorly.

Example prompt: List the missing statesShow

Prompt

Here are the components and screens in this design:
[list them, with the states I have already designed for each]

For each one, list the states that are missing from: default, hover, focus, active, disabled, loading, empty, error, success, read-only, selected.

Only list a state if it applies to that component. For each missing state, say in one line what would trigger it.

Sort by the states users are most likely to hit.
Why this prompt works, and what to verify

Handoff

When to use AI. The design is settled and needs writing up for the people building it.

Good use

"Document this component's behaviour. Put anything I have not specified under open questions."

Weak use

"Write the spec."

What to give the AI

  • Annotated frames or a description
  • States and interactions you have decided
  • Breakpoints

What to review afterwards

  • Every line: did you decide it?
  • The open questions, answered by you

What the designer still owns

  • The behaviour
  • Answering open questions
  • The conversation with the developer

Common failure

A complete-looking spec in which the model filled the gaps with plausible behaviour nobody chose.

Example prompt: Document component behaviourShow

Prompt

Write behaviour documentation for [component or screen] for the developers building it.

What I have designed:
[describe, or attach frames with annotations]

Write:
1. Purpose, in one sentence.
2. States, and what triggers each.
3. Interactions: click, keyboard, touch.
4. Content rules: limits, truncation, wrapping.
5. Responsive behaviour at each breakpoint.
6. Open questions, where my design does not say.

Only document what I have described. Put anything you would have to assume under Open questions, not in the spec.
Why this prompt works, and what to verify