Best AI Prompts for Accessibility Audits with Claude

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We provide expert-crafted AI prompts to revolutionize accessibility audits using Claude. Our methodology shifts accessibility checks left, analyzing design descriptions for visual, motor, and cognitive barriers before code is written. This approach saves time, reduces costs, and ensures genuinely inclusive digital experiences.

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Revolutionizing Accessibility Audits with AI

How many times have you launched a feature, only to discover later that it’s completely unusable for a segment of your audience? It’s a frustrating and costly mistake that goes beyond bad press—it’s a fundamental failure of design empathy. Digital accessibility isn’t just about checking boxes for legal compliance like the ADA or EAA; it’s a core business imperative that expands your market reach and demonstrates a commitment to all users. The most effective way to tackle this is by “shifting left”—integrating accessibility checks at the earliest stages of design and development. Catching an exclusionary design flaw in a wireframe saves hundreds of hours and thousands of dollars compared to fixing it in production code.

This is where an AI co-pilot becomes a revolutionary tool. While a human expert remains the ultimate authority, an AI like Claude acts as a tireless, context-aware first-pass auditor. Its massive context window allows it to “hold” an entire complex user flow or detailed design description in its memory, analyzing it holistically for potential barriers. It can instantly identify exclusion points that a manual check might miss, such as a cognitive overload in a multi-step form or a motor-skill challenge in a custom UI control. Think of it as a brilliant accessibility intern who works in seconds, not days, flagging issues so you can focus your expert judgment on crafting the perfect solution.

This guide provides a practical blueprint for leveraging this power. We will provide a library of ready-to-use prompts designed to analyze design descriptions and user flows for specific disability types—visual, motor, and cognitive. More importantly, we’ll explain the methodology behind these prompts, so you can adapt and create your own. You’ll learn how to seamlessly integrate this AI-driven approach into your existing workflow, transforming your team from reactive bug-fixers into proactive creators of genuinely inclusive digital experiences from the very first sketch.

Section 1: Foundational Prompts for UI/UX Design Descriptions

Ever spent hours in a design review, confident you’ve covered every base, only to have a user with a motor impairment report they can’t even complete the core task? The gap between “it looks good to me” and “it works for everyone” is where most accessibility failures happen. This gap is precisely where a well-structured AI audit becomes your most powerful ally. By feeding detailed design descriptions or user flows to a model like Claude, you can simulate the experience of users with different disabilities and uncover exclusion points before a single line of code is written. This section provides the foundational prompts to start that process, transforming your design descriptions into robust accessibility checklists.

Prompting for Visual Impairment Analysis

Visual accessibility goes far beyond simply adding alt text. It’s about ensuring that information is perceivable through multiple means, not just color or sharp vision. When you’re working with a text-based description of a UI, your goal is to force the AI to think like a user who might have low vision, color blindness, or requires significant text scaling. A common mistake developers make is relying on visual cues that are imperceptible to these users. For instance, a design might use a red border to indicate an error and a green one for success. While clear to a sighted user, this is completely failed for someone with red-green color blindness.

To get a comprehensive analysis, you need to provide the AI with both the UI description and the specific visual context it needs to evaluate. This is a classic “garbage in, garbage out” scenario. A vague prompt yields a vague answer. A detailed prompt yields actionable insight.

Here is a core prompt designed to identify these issues:

“Act as an expert accessibility consultant. I will provide you with a description of a user interface element. Your task is to analyze it for potential barriers for users with visual impairments, including low vision and color blindness.

UI Description: [Insert your detailed UI description here. Example: ‘A user profile settings modal. At the top, there is a red text message that says ‘Invalid email format’. Below the email input field, which has a red border, there is a ‘Save Changes’ button. The button is gray text on a light gray background.’]

Your Analysis Should Cover:

1. Color Contrast: Does the color combination of text and its background meet WCAG AA standards (4.5:1 for normal text)? Identify any text or icons that fail this.
2. Reliance on Color Alone: Does the design use color as the only way to convey information, state, or an action? (e.g., an error state indicated only by a red border).
3. Text Scalability: Based on the description, are there any elements (like fixed-size icons or tightly packed text) that would likely break or become unusable if the user scales their text to 200%?

For each issue found, provide a specific, actionable recommendation for improvement.”

This prompt structure forces the AI to move beyond a simple checklist and engage in critical thinking. It provides a framework (contrast, color reliance, scalability) that mirrors a real-world audit process, ensuring you get consistent and comprehensive feedback every time.

Identifying Motor Accessibility Hurdles

Motor accessibility is often overlooked in the early design stages, yet it’s critical for users who rely on keyboards, switch devices, or voice controls to navigate. A beautiful interface that requires a precise, rapid mouse movement is fundamentally inaccessible to a segment of your audience. The most common failure points here are small touch targets, reliance on complex mouse-only gestures (like drag-and-drop without a keyboard alternative), and a complete lack of keyboard focus indicators.

When describing interactive elements for the AI, you must be explicit about their behavior. Don’t just say “a dropdown menu.” Say “a dropdown menu that opens on click and requires a mouse hover to select an option.” This specificity is what allows the AI to identify the motor-skill challenge.

Consider this prompt to evaluate a design for motor accessibility hurdles:

“Analyze the following user flow description from a motor accessibility perspective. Identify any steps that would be difficult or impossible for a user who only uses a keyboard, a single-switch device, or has limited motor control.

User Flow Description: [Insert your user flow here. Example: ‘To select a date, the user must click a calendar icon. This opens a date picker. To select a date, the user must click on the specific day. There is no keyboard support to navigate between days. The ‘Submit’ button is a small icon without a text label.’]

Please evaluate based on these criteria:

• Keyboard-Only Navigation: Can every interactive element (links, buttons, form fields) be reached and activated using only the Tab and Enter keys? Is there a visible focus indicator?
• Touch Target Size: Are buttons and clickable areas large enough to be easily tapped? (Recommend a minimum of 44x44 CSS pixels).
• Complex Gestures: Does the flow require any complex or timed actions (e.g., dragging, double-tapping, long-pressing) that don’t have a simple, single-action alternative?

Provide a list of identified issues and suggest specific, WCAG-compliant solutions.”

A “golden nugget” of experience here is to remember that the AI can’t “see” a focus state unless you describe it. If your description mentions a “subtle blue glow” on hover, you should explicitly ask the AI, “Is this glow sufficient for a keyboard user to track their position?” This prompts the AI to apply its knowledge of accessibility standards (like the requirement for a 3:1 contrast ratio for focus indicators) to your specific design choice.

Assessing Cognitive Load and Clarity

Cognitive accessibility is about reducing the mental effort required to use a product. A cluttered interface, ambiguous instructions, or an unpredictable layout can be overwhelming for users with cognitive disabilities like ADHD, dyslexia, or anxiety. It can also frustrate any user who is tired, stressed, or new to your product. The goal is to create a clear, simple, and predictable experience.

When prompting for cognitive load, you’re asking the AI to act as a ruthless editor. You want it to flag jargon, identify visual noise, and point out where the user might feel “stuck” or confused. For example, a form with 20 fields and no clear sections is a cognitive burden. A button that says “Go” in one place and “Proceed” in another for the same action is unpredictable.

Use this prompt to analyze your designs for cognitive clarity:

“Act as a UX writer and cognitive psychology expert. Review the following UI description and identify potential sources of high cognitive load or confusion for the user.

UI Description: [Insert your UI description here. Example: ‘A dashboard with 15 different widgets, all using different chart types. The main navigation has labels like ‘Synergy,’ ‘Leverage,’ and ‘Optimize.’ A form asks for ‘User Alias’ but the help text doesn’t explain what that is.’]

Analyze for the following:

• Clarity of Instructions: Are all labels, instructions, and error messages clear, concise, and free of jargon?
• Predictability: Is the user interface consistent in its layout and behavior? Do interactive elements behave as expected?
• Distraction & Visual Noise: Does the design contain unnecessary elements, animations, or dense information that could distract the user from their primary task?

Based on your analysis, provide a prioritized list of recommendations to simplify the design and reduce the user’s cognitive burden.”

By systematically applying these three prompts to your foundational design descriptions, you build a powerful, proactive accessibility check into your workflow. You’re no longer waiting for a post-launch audit to find critical flaws; you’re finding and fixing them at the stage where change is cheapest and easiest.

Section 2: Advanced Prompts for Analyzing User Flows and Journeys

Moving beyond static components, the real accessibility challenges often emerge in the flow—the dynamic journey a user takes through your product. A single page might be perfectly accessible in isolation, but the sequence of actions required to complete a task can create a minefield of barriers. This is where you need to shift your thinking from “Is this element accessible?” to “Can a user with a specific disability successfully complete this entire process?”

This section provides you with advanced prompts designed to simulate these journeys, identify drop-off points, and uncover subtle but critical issues in user flows that a standard component-level audit would completely miss.

Mapping the Entire User Journey for Barriers

A user flow is a story. For a user with a motor impairment, it’s a story of physical endurance. For a user with cognitive challenges, it’s a story of mental load. Your job is to find the plot holes before they cause your users to abandon the story. The following prompt forces you to articulate the flow in a structured way, allowing the AI to analyze it step-by-step.

The Prompt:

“Act as an accessibility consultant. I will provide a multi-step user flow. For each step, analyze it from the perspective of users with different disabilities (visual, motor, cognitive). Identify potential barriers, points of frustration, or drop-off risks. Suggest specific, actionable improvements for each identified issue.

User Flow:

1. Sign Up: User lands on the homepage, clicks ‘Sign Up’, is presented with a modal containing three fields (Name, Email, Password), and a ‘Submit’ button.
2. Email Verification: After submitting, the user sees a message ‘Check your email for a verification link’. The email contains a single link they must click.
3. Onboarding: Upon clicking the link, the user is taken to a 5-step onboarding wizard. Each step has a ‘Next’ and ‘Back’ button. The wizard includes a progress bar at the top.
4. First Purchase: After onboarding, the user is taken to the dashboard. They must click ‘Shop Now’, select a product, add it to the cart, and complete the checkout form (Address, Payment).”

Why This Works (The Expert Insight):

This prompt structure is effective because it breaks a complex journey into discrete, analyzable moments. When you provide this to an AI, it can identify patterns a human might overlook in a manual review:

• Motor Impairment: The AI will flag the modal as a potential trap for keyboard-only users if focus isn’t managed correctly. It will also highlight the repetitive “Next/Back” button clicking in the wizard as a significant physical burden.
• Cognitive Load: It will point out that asking a brand new user for a password and immediately forcing them to verify an email before they can do anything is a major friction point. It will also flag the 5-step onboarding wizard as overwhelming, suggesting it could be broken down or made skippable.
• Visual Impairment: The AI will note that a simple text message “Check your email” provides no context for a screen reader user who might have multiple email accounts. It will also question if the progress bar is properly coded with ARIA attributes to announce its status to screen reader users.

Golden Nugget: The most common failure point in user flows isn’t a lack of alt text; it’s context switching. Every time you force a user to switch from your app to their email and back again, you create a cognitive and motor hurdle. For users with disabilities, this is often the point of no return. Your primary goal should be to keep them in a single, predictable context.

Simulating the Screen Reader Experience

One of the biggest disconnects in accessible design is the gap between what we think a screen reader user hears and what they actually hear. We see a visually rich page; they get a linearized, stripped-down stream of information. By asking an AI to role-play this experience, you can instantly identify confusing navigation, unhelpful labels, and illogical reading order.

The Prompt:

“I want you to simulate the experience of a screen reader user navigating a web page. I will provide you with the page’s HTML structure and key text content. Your job is to ‘read’ the page aloud, line by line, as a screen reader would. Announce headings, links, buttons, and form fields in the order they appear. After the simulation, identify any confusing elements, redundant announcements, or illogical navigation paths. Highlight any images that lack descriptive alt text or have unhelpful alt text.

Page Content: [Paste your HTML structure here. For example: <header><h1>Dashboard</h1></header> <main><h2>Welcome, Alex!</h2><p>Here is your latest activity.</p><img src="chart.png"><p>Yesterday, you completed 5 tasks.</p><a href="/tasks">See all tasks</a></main>]”

Why This Works (The Expert Insight):

This simulation is a powerful empathy-building and debugging tool. A designer might look at the HTML above and think it looks fine. But the AI’s simulation might reveal:

• “Heading level 2: Welcome, Alex! Here is your latest activity.” (The heading is immediately followed by a paragraph, which can be jarring).
• “Graphic: chart.png” (The lack of alt text is immediately obvious and frustrating).
• “Link: See all tasks” (This is better than “click here,” but what tasks? Is this the only way to get there?).

By forcing the AI to read the content linearly, you are forced to confront the non-visual reality of your design. You’ll quickly learn to structure your HTML not for visual layout, but for logical, sequential reading.

Testing for Seizure and Vestibular Disorder Triggers

In 2025, with higher refresh rates and more dynamic UIs, motion is a bigger accessibility concern than ever. Animations that are “delightful” to most can be physically painful or dangerous for others. This prompt helps you audit motion without needing to build a prototype.

The Prompt:

“Analyze the following description of animations and transitions on a webpage. Flag any elements that could pose a risk for users with photosensitive epilepsy or vestibular disorders. For each risk, explain the specific WCAG guideline it violates and suggest a safer alternative.

Animation Descriptions:

1. Page Load: When the main content area loads, all elements fade in sequentially with a ‘bounce’ effect over 1.5 seconds.
2. Image Carousel: An auto-playing hero banner that slides left every 3 seconds. The user cannot pause or stop the movement.
3. Hover Effect: Hovering over a navigation link triggers a rapid, pulsating color change (red to yellow) that flashes 4 times in under one second.
4. Parallax Background: The background image scrolls at a different speed than the foreground content as the user scrolls down the page.”

Why This Works (The Expert Insight):

This prompt translates subjective design descriptions into objective accessibility risks. The AI will correctly identify:

• The Bounce Effect: This can trigger vestibular issues like nausea or dizziness due to its exaggerated, unpredictable movement.
• The Auto-Playing Carousel: This is a major violation of WCAG 2.2’s “Pause, Stop, Hide” criterion. It removes user control, a critical principle.
• The Pulsating Hover Effect: The AI will flag this as a severe seizure risk because it flashes too frequently and in high-contrast colors. This is a direct violation of WCAG’s “Three Flashes or Below Threshold” rule.
• The Parallax Effect: While not always a violation, the AI should note that this can be disorienting and difficult for users with vestibular disorders to control, advising that it should be paired with a user preference to reduce motion.

By using these advanced prompts, you’re not just finding bugs; you’re fundamentally changing your design process to be more inclusive from the start. You’re simulating real-world user experiences at scale, allowing you to build products that are not just compliant, but genuinely usable and safe for everyone.

Section 3: The “Red Team” Accessibility Prompt: A Case Study

What if you could intentionally stress-test your design before a single line of code is written? This is the core principle of Red Teaming for accessibility. Instead of a passive checklist, you adopt an adversarial mindset, actively hunting for every conceivable barrier. You’re not just asking “Is this accessible?” You’re asking, “How can I break this for a user with a disability?” This proactive approach uncovers the subtle, complex issues that standard audits often miss. To do this effectively with an AI, you need a prompt that forces it into a specific, critical persona.

The Master “Red Team” Prompt

This prompt is engineered to transform Claude from a helpful assistant into a seasoned, slightly cynical accessibility consultant whose entire job is to find flaws. It sets a clear mission, defines the expert persona, and demands a specific output format for maximum utility.

Copy and paste this master prompt to begin your audit:

Act as a world-class accessibility consultant with 20+ years of experience, specializing in WCAG 2.2 AA/AAA compliance and inclusive design principles. Your persona is a “Red Teamer”—your sole purpose is to find flaws, not praise what’s working. You are cynical, meticulous, and you assume every design choice is a potential barrier until proven otherwise.

I will provide you with a description of a user flow or UI design. Your task is to perform a deep, critical audit based on this description.

For each potential issue you identify, you MUST provide the following in your response:

1. The Barrier: A clear, concise statement of the problem.
2. Affected Users: Specify the disability type (e.g., motor, visual, cognitive, auditory) and the specific user persona (e.g., “screen reader user,” “keyboard-only user with tremors,” “user with dyscalculia”).
3. WCAG Violation: Cite the specific WCAG 2.2 success criterion (e.g., “2.1.1 Keyboard,” “3.3.2 Labels or Instructions,” “2.4.3 Focus Order”).
4. Severity: Rate the issue as Critical, High, Medium, or Low.
5. Potential Fix: A brief, actionable suggestion for remediation.

Here is the design to audit:

[PASTE YOUR DESIGN OR USER FLOW DESCRIPTION HERE]

Case Study: Auditing a Complex E-Commerce Checkout Flow

To see this prompt in action, let’s use a typical, modern e-commerce checkout flow that on the surface seems robust and user-friendly.

The Design Description:

“A multi-step checkout process for an online store. The user starts on a ‘Guest Checkout’ or ‘Sign In’ page. The flow is divided into four steps: Shipping Information, Billing Information, Review Order, and Payment. A progress indicator at the top shows the four steps, with the current step highlighted in green. The ‘Shipping Information’ form includes fields for Name, Address, and Phone. The Address field uses an auto-complete feature that suggests addresses as the user types. If the user enters an invalid phone number, an error message appears in red text below the field. The ‘Payment’ step features a custom-built credit card input form with separate fields for card number, expiry, and CVV. To enter the CVV, the user must click a small ‘What’s this?’ icon, which opens a modal explaining the security code. The final ‘Place Order’ button is a large, green, visually prominent button.”

After feeding this description into the master prompt, Claude’s “Red Team” audit would likely produce a report like this:

Claude’s “Red Team” Output (Abridged):

1. The Barrier: The progress indicator is purely visual. The step names and current status are not programmatically announced to assistive technology.

   • Affected Users: Screen reader users.
   • WCAG Violation: 1.3.1 Info and Relationships.
   • Severity: High.
   • Potential Fix: Use an ordered list (<ol>) for the steps and aria-current="step" on the current list item.

2. The Barrier: The address auto-complete suggestions may not be accessible via keyboard, trapping users who cannot use a mouse.

   • Affected Users: Keyboard-only users, users with motor impairments.
   • WCAG Violation: 2.1.1 Keyboard.
   • Severity: Critical.
   • Potential Fix: Ensure the autocomplete list is fully keyboard-navigable (using arrow keys, Enter to select, and Esc to dismiss) and that focus is managed correctly between the input and the list.

3. The Barrier: The error message “appears in red text.” If this is the only indicator of an error, it’s invisible to screen readers and colorblind users.

   • Affected Users: Screen reader users, users with low vision, colorblind users.
   • WCAG Violation: 1.4.1 Use of Color; 3.3.1 Error Identification.
   • Severity: High.
   • Potential Fix: Programmatically associate the error message with the input field using aria-describedby and aria-invalid="true". Ensure the message text is descriptive.

4. The Barrier: The “What’s this?” icon for the CVV is likely an inaccessible SVG or icon font without a proper text alternative, making its purpose unknown to screen readers.

   • Affected Users: Screen reader users.
   • WCAG Violation: 2.5.5 Target Size (if too small) and 4.1.2 Name, Role, Value.
   • Severity: Medium.
   • Potential Fix: Add an aria-label to the button (e.g., aria-label="What is a CVV?") and ensure the resulting modal is keyboard-trap-safe.

Actionable Remediation from AI Feedback

The “Red Team” audit gives you a prioritized hit list. Now, let’s translate the top issues into concrete fixes for your designers and developers.

1. Fixing the Inaccessible Progress Indicator

• The Problem: The visual indicator is just a picture to a screen reader.
• The Solution: Refactor the HTML to use semantic structure.
  • Design: Add a visual “checkmark” or “completed” state for steps, but also ensure the text contrast ratio is at least 4.5:1.
  • Development: Wrap the steps in an <ol> (ordered list) element. On the <li> for the current step, add the attribute aria-current="step". This tells the screen reader, “You are currently on the ‘Shipping Information’ step, which is step 1 of 4.”

2. Solving the Keyboard Trap in the Address Autocomplete

• The Problem: The mouse-only dropdown is an impassable wall for keyboard users.
• The Solution: Implement a robust keyboard interaction model.
  • Design: Design the state for when a user has focused on an item in the dropdown list (e.g., a blue background on the text). This is a focus indicator.
  • Development: Use aria-owns on the input to programmatically link it to the list of suggestions. Ensure the Enter key selects the highlighted suggestion and Escape closes the list. Golden Nugget: A common mistake is to close the list on Tab. Don’t. The user should be able to Tab out of the autocomplete to the next form field without the list interfering.

3. Making Error Messaging Programmatic and Clear

• The Problem: A red border or text alone is not an error message; it’s a color choice.
• The Solution: Create a robust, two-part error system.
  • Design: The error message text should be clear and instructional (e.g., “Please enter a valid 10-digit phone number,” not just “Invalid input”). The design must also account for the error state visually (e.g., a red border, an error icon next to the field).
  • Development: This is where the code makes it accessible. When an error occurs:
    1. Add aria-invalid="true" to the <input> element.
    2. Add a unique ID to your error message <span> (e.g., id="phone-error").
    3. On the <input>, add aria-describedby="phone-error". This creates a direct, programmatically announced link between the input and its error message, so a screen reader will announce “Phone number, invalid entry, Please enter a valid 10-digit phone number.”

Section 4: Integrating AI Audits into Your Development Workflow

So, you’ve generated a list of potential accessibility issues with Claude. What’s next? The true power of an AI co-pilot isn’t in generating a one-off report; it’s in weaving its intelligence directly into the fabric of your development process. This is how you move from reactive bug-fixing to proactive, inclusive design. Let’s transform those AI insights into a living, breathing part of how your team builds products.

Operationalizing AI with a Standardized Accessibility Checklist

The biggest mistake teams make is treating AI output as a final verdict. Instead, treat it as the foundation for a standardized checklist that evolves with your project. This checklist becomes your team’s shared language for accessibility, ensuring everyone—from product managers to junior developers—is thinking about inclusion from day one.

Here’s how to structure it based on the prompt categories we’ve discussed:

• Pre-Design & Wireframing (Cognitive & Flow Focus):

  • AI Check: Have we run our “Cognitive Load” prompt on the user flow description? Are there more than three critical decisions in a single view?
  • AI Check: Does the “Motor-Skill Flow” prompt flag any steps that require sustained, precise movements (e.g., drag-and-drop without an alternative)?
  • Human Review: Does the proposed flow align with established mental models for our user base?

• UI Component Design (Visual & Motor Focus):

  • AI Check: Run the “Visual Contrast” prompt on all new UI elements. Are all text and interactive elements meeting WCAG AA standards?
  • AI Check: Use the “Motor-Skill Interaction” prompt on custom controls (e.g., carousels, custom dropdowns). Does the AI suggest keyboard-only alternatives?
  • Human Review: Are focus states clearly visible and logically ordered?

• Pre-Launch QA (Holistic Flow & ARIA):

  • AI Check: Feed the complete, end-to-end user journey into the “Holistic Journey” prompt. Does the AI identify any potential “traps” where a user could get stuck?
  • AI Check: Run the “Advanced Auditor” prompt on dynamic components (modals, accordions, notifications). Does the AI provide specific aria-* attribute recommendations?
  • Human Review: Conduct a full manual keyboard-only and screen reader test.

Golden Nugget: Don’t just use the checklist as a gate. Attach the actual AI output to the ticket. This provides developers with the AI’s reasoning, turning a simple “fix this” into a valuable learning moment.

Collaborating with Human Experts: AI as a Force Multiplier, Not a Replacement

This is the most critical principle: AI is a tool, not an accessibility expert. Its value lies in augmenting human expertise, not replacing it. A human tester brings context, empathy, and the ability to discover “unknown unknowns” that a prompt can’t anticipate.

Think of it as a “triage” model. The AI performs a rapid, high-level scan, flagging hundreds of potential issues in seconds. This allows your human experts to focus their valuable time and cognitive energy on the nuanced, complex problems that require true judgment.

Here’s the collaborative workflow:

1. AI as the Scout: The AI performs the initial pass, generating a raw list of potential violations and suggestions. This output is then used to prepare for manual audits.
2. Human as the Strategist: The accessibility specialist reviews the AI’s findings, prioritizing them based on user impact. They use the AI’s output to build a more targeted and efficient manual testing plan.
3. AI as the Documenter: The AI’s clear, structured output can be used to instantly create draft documentation for bug reports or accessibility conformance reports (ACRs). The human expert then refines this with their specific findings and context.

This approach also serves as a powerful educational tool. When a junior developer sees that an AI flagged a contrast issue and can explain why it fails WCAG 1.4.3, they learn faster than if they were just told to “make the button darker.”

Prompt Engineering Best Practices for Audits

To get the most out of your AI co-pilot, you need to move beyond basic prompts. Like any powerful tool, the quality of your input directly determines the quality of your output. Here are advanced techniques to elevate your AI audits:

• Provide Richer Context: Instead of just describing a component, describe the user and the environment.

  • Basic: “Analyze this login form for accessibility.”
  • Advanced: “Analyze this login form for a user with low vision who relies on a screen magnifier and a keyboard. The user is in a distracting environment and is over 60 years old. Focus on cognitive load and motor-skill challenges.”

• Specify the WCAG Conformance Level: Ask the AI to check against specific criteria. This forces it to be more precise and provides developers with direct references.

  • Advanced Prompt: “Review this data table component. Identify any violations against WCAG 2.1 Level AA criteria 1.3.1 (Info and Relationships) and 2.1.1 (Keyboard).”

• Iterate and Refine: Your first prompt is a starting point. Use the AI’s output to ask better follow-up questions.

  • Initial Output: “The button’s color contrast may be insufficient.”
  • Your Follow-up Prompt: “You flagged the button contrast. The background is #4A90E2 and the text is #FFFFFF. Calculate the exact contrast ratio and suggest three alternative background colors that meet WCAG AA while staying within our brand palette.”

• Assign a Persona: Instructing the AI to act as an expert can often yield more detailed and nuanced results.

  • Advanced Prompt: “You are a senior accessibility consultant specializing in WCAG 2.2 and cognitive accessibility. Audit this user onboarding flow and provide a prioritized list of recommendations, citing relevant success criteria.”

By mastering these techniques, you’re not just getting a checklist of problems; you’re engaging in a dialogue that deepens your team’s understanding of accessibility principles. This transforms the AI from a simple tool into a true partner in building a more inclusive web.

Conclusion: Building a More Inclusive Web, One Prompt at a Time

We’ve journeyed from the fundamentals of describing UI elements to the complexities of auditing entire user flows and even running “Red Team” exercises to find the subtle, critical flaws that standard checks miss. The core takeaway is that AI like Claude isn’t just a code reviewer; it’s a versatile accessibility consultant. Whether you’re analyzing a static design description, a multi-step journey, or a dynamic ARIA challenge, the power lies in asking the right questions. This approach transforms accessibility from a daunting checklist into a collaborative, insightful process.

Looking ahead to the rest of 2025 and beyond, the role of AI in this space is set to become even more deeply integrated. We’re moving toward a future where AI doesn’t just identify issues but can suggest and even implement code-level remediations in real-time within our IDEs. Imagine an AI co-pilot that flags a missing aria-expanded attribute and provides the exact code snippet, complete with the necessary JavaScript event handlers. The future isn’t about replacing human judgment but about augmenting it, allowing us to focus on complex user experience problems while the AI handles the pattern recognition at scale.

Accessibility isn’t a feature you add at the end; it’s a fundamental aspect of quality you build in from the start.

The journey to creating truly inclusive digital products is ongoing, but you don’t need to overhaul everything at once. The most impactful changes start small. Your call to action is simple but powerful: choose one critical user flow in your current project—a login form, a checkout process, or a settings page—and run it through one of the advanced prompts we’ve discussed. Analyze the feedback, discuss it with your team, and implement one key improvement this week. By making this a regular practice, you’re not just fixing bugs; you’re building a culture of accessibility and making the web a better place for everyone.

Critical Warning

Frequently Asked Questions

Q: Why is Claude specifically recommended for accessibility audits

Claude’s massive context window allows it to analyze entire complex user flows or detailed design descriptions holistically, identifying systemic accessibility issues that smaller context models might miss

Q: How does ‘shifting left’ save money in accessibility

Fixing an accessibility flaw in a wireframe costs pennies compared to fixing it in production code. Early detection prevents expensive rework and legal risks

Q: Can AI replace human accessibility experts

No, AI acts as a tireless first-pass auditor or ‘accessibility intern’ to flag potential issues. Human expertise remains essential for final judgment, nuanced context, and legal compliance verification