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For Designers

Reduce research synthesis time from 8 hours to 1 hour by using AI to 1 hour by.

Q: How do professionals in this role use AI prompts to improve their work?

AI prompts help professionals automate repetitive tasks, synthesize information, generate content, analyze data, and make better decisions. By providing clear context and specific requirements, AI becomes a powerful assistant that saves time and improves quality.

Q: What are the most useful AI prompts for this role?

The most useful prompts depend on your specific responsibilities, but generally include prompts for documentation, analysis, communication, planning, and problem-solving. Our role-specific prompts are designed to address the unique challenges and workflows of each role.

Q: How can I get started with AI prompts for my role?

Start by browsing our role-specific prompts, identify 2-3 prompts that address your biggest pain points, customize them for your context, and integrate them into your daily workflow. Our prompts include examples and best practices to help you get started quickly.

When you say "Act as a UX Designer" to AI, you're creating a research partner that can synthesize qualitative data, build personas, write microcopy, and bridge the designer-developer handoff gap. AI becomes your research analyst, persona generator, and handoff specialist.

8 Prompts 1 Patterns

What Does a Product Designer (UX/UI) Do?

The Product Designer's role is to be the "advocate for the people", focusing on making products usable, enjoyable, and accessible. In an enterprise context, designers deal with significant data complexity, navigate legacy systems, and understand highly specialized, niche workflows.

Synthesizing user research

Creating user personas

Writing UX microcopy

Designer-developer handoff

Designing for enterprise complexity

Conducting user interviews

How AI Transforms UX/UI Design

Performs thematic analysis on user interview transcripts

Creates detailed personas focused on goals and pain points

Generates clear, concise UX microcopy

Translates designs into developer-ready user stories and ACs

Brainstorms layouts and data visualizations for complex dashboards

Example: A designer can paste 10 user interview transcripts, and AI identifies the top 3 usability pain points with supporting quotes—work that would take hours of manual analysis.

Resources for Designers

Workflows, guardrails, AI pain points, and recommendations tailored to your role

high Guardrail

Prevent Type Coercion Errors In Batch Processing

AI-generated code in dynamically-typed languages (like JavaScript) often uses implicit type coercion (e.g., 1 + "2" = "12"), which causes silent data corruption in batch processing jobs.

View Guardrail

high Guardrail

Prevent Silent Data Truncation

AI-generated code (e.g., for migrations or ETL jobs) may miscalculate field lengths or types, causing data to be silently truncated (e.g., VARCHAR(255) to VARCHAR(50)) without raising an error.

View Guardrail

Recommendation

Start with Few-Shot Learning for Beginners

For teams new to AI, zero-shot (no examples) prompts are unreliable for complex tasks. Few-shot learning, where you provide 2-5 examples in the prompt, is a simple, highly-effective technique to guide the AI, improve accuracy, and make powerful AI tools more accessible to all developers.

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Recommendation

Enforce Small PRs for AI-Generated Code

AI tools make it easy to generate thousands of lines of code in seconds. This often leads to "AI-slop" PRs that are so large they are impossible to review, hiding bugs and security flaws. This practice destroys team velocity and code quality by creating massive review bottlenecks.

Learn More

View All 6 Guardrails View All 8 Recommendations

Common Problems & AI Solutions

How AI prompt engineering solves real challenges

Synthesizing Qualitative User Research into Actionable Insights

The designer conducts user interviews but is then left with hours of transcripts or raw notes. The challenge is finding the patterns and "key themes" in this unstructured, qualitative data.

Solution: The "Qualitative Data Analyst": Ingests raw interview transcripts to perform thematic analysis and extract key user pain points.

Creating Detailed and Representative User Personas

Personas are a core UX tool, but they are often created poorly, focusing on irrelevant demographic data. A valuable persona focuses on goals, motivations, behaviors, and pain points.

Solution: The "Persona Generator": Builds personas from research themes, focusing on goals, motivations, and pain points, not just demographics.

Writing Clear and Effective Microcopy (UX Writing)

Designers often use "Lorem Ipsum" as placeholder text, but the copy is a critical part of the design. "UX Writing" is a specialized skill, especially for complex enterprise apps.

Solution: The "UX Writing Assistant": Generates clear, concise, and on-brand copy for UI states (e.g., error, success, empty) from a simple description.

The Designer-Developer Handoff

This is a massive cross-functional bottleneck. Designers and developers "speak different languages". Developers frequently receive designs that are "missing screens or logic" and lack context for interaction, errors, or edge cases.

Solution: The "AI Handoff Specialist": Translates a design mockup (or description) into the full set of developer-ready assets: user stories, ACs, and interaction notes.

Designing for Complex, Data-Heavy Enterprise Applications

Enterprise UX is not consumer UX. It is defined by high data complexity, the need for efficiency for expert users, and user "journeys that could wrap a building".

Solution: The "Information Architect" Brainstorming Partner: Suggests layouts, data visualizations, and component ideas for complex, data-heavy dashboards.

Key Activities

What designers do and how AI helps

documentation

Design System Documentation Generator

Analyzing 10 user interview transcripts to identify top 3 pain points

Creating a persona from research themes focusing on goals and motivations

Generating UX microcopy for error, success, and empty states

Translating design mockup into complete user story and acceptance criteria

Brainstorming 3 different dashboard layouts for enterprise data

Extracting key themes from qualitative research with supporting quotes

Identifying missing states and edge cases in design handoffs

Success Stories & Patterns

How professionals use AI and patterns that work

A designer reduced research synthesis time from 8 hours to 1 hour by using AI to analyze interview transcripts

A designer created 5 personas in one session using AI, ensuring consistency across the design team

A designer generated all microcopy for a feature in 15 minutes, ensuring consistent tone and clarity

A designer improved handoff quality by 90% by having AI generate complete user stories and ACs from designs

Template

Featured Prompts

Top prompts for designers

Creating Detailed and Representative User Personas

Personas are a core UX tool, but they are often created poorly, focusing on irrelevant demographic data. A valuable persona focuses on goals, motivations, behaviors, and pain points.

general

persona-generator

View Prompt

Designing for Complex, Data-Heavy Enterprise Applications

Enterprise UX is not consumer UX. It is defined by high data complexity, the need for efficiency for expert users, and user "journeys that could wrap a building".

architecture

information-architect

View Prompt

Writing Clear and Effective Microcopy (UX Writing)

Designers often use "Lorem Ipsum" as placeholder text, but the copy is a critical part of the design. "UX Writing" is a specialized skill, especially for complex enterprise apps.

general

ux-writing-assistant

View Prompt

The Designer-Developer Handoff

architecture

ai-handoff-specialist

View Prompt

Synthesizing Qualitative User Research into Actionable Insights

The designer conducts user interviews but is then left with hours of transcripts or raw notes. The challenge is finding the patterns and "key themes" in this unstructured, qualitative data.

general

qualitative-data-analyst

View Prompt

Design System Documentation Generator

Create comprehensive design system documentation that enables consistent design implementation.

documentation

template

View Prompt

User Research Synthesis

Synthesize user research data into actionable insights and design recommendations.

general

template

View Prompt

Design Handoff Checklist

Ensure smooth design-to-development handoff with comprehensive specifications and clear communication.

documentation

template

View Prompt

Recommended Patterns

Patterns most useful for designers

Template

Provides a structured format for the AI to fill in

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Best Practices & Recommendations

Actionable guidance for designers

Recommendation

Start with Few-Shot Learning for Beginners

Learn More

Recommendation

Enforce Small PRs for AI-Generated Code

Learn More

Recommendation

Structure Your AI Prompt Library for Reusability

Individual developers and teams will inevitably waste hours "reinventing the wheel" by creating and refining prompts for common tasks. This duplicate work is inefficient, expensive, and leads to inconsistent AI usage and outputs across the organization.

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Recommendation

Always Validate AI Suggestions Before Merging

AI-generated code often looks plausible but contains subtle logic errors, security vulnerabilities, or performance bottlenecks.5 Blindly trusting and merging this code is dangerous, erodes quality, and creates massive, hidden technical debt.

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Recommendation

Use Test-Driven Development with AI-Generated Code

Relying on AI to generate code and tests at the same time is risky. The AI can misinterpret requirements and produce code with subtle bugs. Manually reviewing large blocks of AI-generated code is slow and error-prone. Test-Driven Development (TDD) provides the perfect framework to solve this.

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Recommendation

Implement a Formal AI Literacy Framework for All Technical Roles

Implement a formal, multi-level AI literacy framework to build durable skills across the entire organization. AI literacy is a new core competency that is not limited to engineers. It emphasizes critical thinking, ethical reasoning, and the ability to evaluate AI outputs, which are essential skills to mitigate bias, reduce privacy risks, and build resilient, trust-based AI workflows.

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Recommendation

Mandate Secure Prompt Engineering Practices for All Developers

Mandate the use of secure prompt engineering practices as the first line of defense in the AI-assisted development lifecycle. The prompt is the new "shift-left"; a vague or naive prompt will predictably generate insecure code, while an explicit, security-aware prompt will produce safer, more robust outputs. This practice is a form of proactive risk mitigation, not just an output-optimization technique.

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Recommendation

Use AI for Automated Test Generation and Self-Healing Maintenance

Integrate AI into the quality assurance (QA) process to automatically generate test cases, optimize test suites, and perform "self-healing" maintenance on brittle automation scripts. This moves AI's role in testing beyond simple TDD (Recommendation 7) and uses it to solve the significant economic and time costs of test maintenance.

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View All Recommendations