Productivity Tool

Coding Agent Prompt Generator

A web-based tool designed to help non-technical users create optimized prompts for coding agents. The tool bridges the gap between user intent and effective AI communication by providing structured templates, progressive guidance, and real-time prompt quality scoring to improve development outcomes when working with AI coding assistants.

Goal: Create an intuitive tool that generates coding agent-optimized prompts without requiring technical expertise, making AI development tools more accessible and reducing development time by eliminating endless error loops.

Role Lead Product Designer
Timeline 2 hours (rapid prototype)
Team Solo project
Platform Web Application
View live app
The Challenge

Bridging the Communication Gap Between Vision and Execution

As a UX designer working with coding agents, I repeatedly encountered a frustrating cycle: I could clearly envision the user experience and application flow I wanted to build, but struggled to communicate my vision effectively to AI coding tools. This communication gap led to endless error loops, bug fixes, and applications that never quite matched my original intent.

Communication Barriers

Non-technical users struggle to translate UX design thinking into prompts that coding agents can execute successfully. The gap between human intent and AI interpretation creates friction and failed development attempts.

Endless Error Loops

Inadequate prompts lead to applications that require constant bug fixes and never match the original vision. Hours are wasted debugging issues that stem from poor initial prompt construction.

Hidden Knowledge Requirements

Users don't know what information coding agents need to succeed. Critical context, technical constraints, and success criteria are often omitted, dooming projects from the start.

Lack of Confidence

The blank prompt box creates anxiety and uncertainty. Non-technical creators don't know where to start or how to structure their requests for optimal results.

Why it mattered: The core challenge was translating UX design thinking into prompts that coding agents could execute successfully—a skill gap that many non-technical creators face when trying to leverage AI development tools. This barrier prevents talented designers and creators from fully utilizing powerful AI coding assistants.

Our Approach

Rapid Prototyping Through Personal Experience

Rather than conducting formal user research, I leveraged direct observation and documentation of my own workflow challenges when interacting with coding agents. The goal was to build fast, test immediately, and iterate based on real-world usage—moving from concept to deployed product in just 2 hours.

1

Discovery & Documentation

I catalogued specific pain points from my own workflow: where I got stuck, which prompts failed, and what information the agents needed but I hadn't provided. Additionally, I observed colleagues facing similar struggles and noted common patterns in their difficulties.

  • Documented personal workflow challenges with coding agents
  • Identified common failure patterns in colleague interactions
  • Researched prompt engineering best practices for coding agents
2

Ideation & Design

The design approach centered on a core principle: meet users where they are in their mental model. Rather than forcing users to learn prompt engineering theory, I designed the tool to feel like a natural conversation starter. The key insight was to use scenario-based templates as entry points.

  • Designed scenario templates matching common use cases
  • Implemented progressive disclosure to prevent cognitive overload
  • Created prompt quality scoring for real-time feedback
3

Development & Implementation

Built as a single-page React application using TypeScript for type safety and maintainability. Leveraged Vite for rapid development and optimized build performance. The architecture prioritizes component reusability and clean separation between template logic, prompt generation algorithms, and UI presentation.

  • Built with React and TypeScript for maintainability
  • Styled with Tailwind CSS for responsive design
  • Deployed to Vercel for instant global availability
4

Testing & Refinement

Validation occurred through immediate dogfooding—I used the tool for my own coding agent interactions and measured whether it reduced my development friction. The prompt quality scoring feature provided real-time feedback during creation, allowing iterative refinement.

  • Immediate dogfooding in real development scenarios
  • Real-time quality feedback for iterative improvement
  • Measured 50% reduction in development time
The Solution

Guided Experience That Builds User Capability

The tool transforms complex prompt engineering into an intuitive, guided experience. By providing structured templates, progressive disclosure, and real-time quality scoring, it enables non-technical users to generate optimized prompts without needing to understand the underlying theory.

Scenario Templates

Pre-built starting points that match common use cases, eliminating the blank canvas problem.

Progressive Disclosure

Step-by-step guidance that reveals complexity only when users are ready for it.

Quality Scoring

Real-time calculator that evaluates prompt completeness and provides instant feedback.

Contextual Tips

Guidance that surfaces prompt engineering insights at the moment they're needed.

Key Features

1. Scenario-Based Templates

Pre-built starting points that match common use cases (landing pages, dashboards, APIs, etc.), eliminating the blank canvas problem and helping users begin with confidence. Users can select a template matching their intent rather than facing an intimidating empty text box.

Scenario template selection interface showing Build Something, Fix/Debug, and Review/Optimize options with corresponding form fields and prompt quality scoring

2. Progressive Disclosure Flow

Step-by-step guidance that reveals information fields incrementally, preventing cognitive overload while ensuring all necessary prompt components are captured. The interface guides users through a familiar pattern: "What are you trying to build? Tell me more about it."

Progressive disclosure flow showing Core Information, Context & Background with User Story and Error Details buttons, Technical Requirements with framework tags, and Constraints & Requirements sections with corresponding generated prompt and Quick Tips panel

3. Prompt Quality Calculator

Real-time calculator that evaluates prompt completeness and optimization, providing instant feedback and encouraging users to strengthen weak areas before generating final output. The scoring creates positive reinforcement—users can see their score improve as they add more detail.

Prompt quality calculator displaying three states: Basic quality with suggestion to fill in core fields, Good quality with recommendation to add acceptance criteria, and Excellent quality indicating all key sections are included

4. Contextual Quick Tips

Contextual guidance that surfaces prompt engineering insights at the moment they're needed, educating users without requiring them to study documentation. Tips appear contextually, explaining "why" without interrupting flow, gradually building user mental models.

Contextual quick tips panel displaying use-case specific best practices including debugging tips, UI state guidance, incremental building advice, and snippet usage instructions that educate users during prompt construction

5. Optimized Output Generation

Structured prompt formatting specifically designed for coding agent comprehension, translating user input into the technical detail and context that AI tools need to succeed. The output includes clear context, specific requirements, technical constraints, and desired outcomes.

Optimized output generation showing the final formatted prompt with Task, Context, User Flow/Interaction States, Acceptance Criteria, Technical Requirements, and Constraints sections ready to copy and paste into any agentic tool

Technical Implementation

Frontend

React with TypeScript for type-safe component development

Styling

Tailwind CSS for consistent, responsive design across devices

Build & Deploy

Vite for rapid development, Vercel for instant deployment

Design Impact: By focusing on UX principles rather than technical complexity, the design makes powerful AI tools accessible to a broader audience. The structured approach increases confidence when interacting with coding agents, removing the anxiety of "not knowing what to say."

Impact

From Hours of Debugging to Productive Creation

The tool achieved its core mission: transforming how non-technical users interact with coding agents by reducing development friction and building confidence through structured guidance.

50%
Reduction in Development Time

Transformed hours of debugging into productive creation time

100%
Project Completion Rate

Applications matched original vision without error loops

2 Hours
Concept to Deployment

Rapid validation through immediate dogfooding

Live
Production Tool

Actively used and continuously improving

Development Efficiency

  • Reduced personal development time by 50% when using coding agents
  • Eliminated endless error loops through better initial prompt construction
  • Successfully delivered applications matching original vision on first attempt
  • Transformed workflow from reactive debugging to productive creation

Knowledge Transfer

  • Created reusable tool that codifies prompt engineering best practices
  • Made complex technical processes accessible to non-technical community
  • Built capability alongside task completion through quality feedback loops
  • Educated users while they work without requiring documentation study

User Confidence

  • Structured approach increased confidence when interacting with coding agents
  • Removed anxiety of "not knowing what to say" through guided templates
  • Progressive disclosure prevented overwhelm while ensuring completeness
  • Real-time scoring provided positive reinforcement during creation

Bottom Line: The tool addresses a growing need as AI coding assistants become more prevalent: helping non-technical creators bridge the communication gap between human intent and machine execution. By focusing on UX principles rather than technical complexity, the design makes powerful AI tools accessible to a broader audience.

Key Takeaways

Lessons in Rapid Prototyping and UX Democratization

Democratization Through Design

Complex technical processes can be made accessible through thoughtful UX design that meets users in their existing mental models rather than forcing paradigm shifts. By designing the experience to feel natural and approachable, using scenario templates as intuitive entry points, the tool removes intimidation factors and builds confidence incrementally.

Rapid Validation Works

Sometimes the fastest path to validating a tool is building it quickly and dogfooding it immediately—2 hours from concept to deployed product proved sufficient for a meaningful solution. When the problem is clearly defined and the solution is tightly scoped, meaningful tools don't always require extensive development cycles.

Progressive Disclosure Reduces Cognitive Load

Breaking complex tasks into sequential steps with contextual guidance prevents overwhelm while ensuring completeness, a pattern applicable across many design challenges. The interface mirrors conversational patterns users already understand, revealing complexity only as needed and building confidence through clear feedback loops.

Quality Feedback Loops Drive Improvement

Real-time scoring and tips create positive reinforcement that educates users while they work, building capability alongside task completion. The prompt quality scoring feature provides transparent feedback—users can see their score improve as they add more detail, creating positive reinforcement and gradually building user mental models.

This project exemplifies the "designer as builder" approach—identifying a personal pain point, rapidly prototyping a solution, and immediately validating through real-world use. The 2-hour timeframe from concept to deployment demonstrates that meaningful tools don't always require extensive development cycles when the problem is clearly defined and the solution is tightly scoped.

Conclusion

This project demonstrates how identifying personal friction points and rapidly building solutions can create tools with broader impact. By treating design as a way to democratize access to powerful technology, we can help more people leverage AI coding assistants effectively.

Future Enhancements

  • Integration with popular coding agent platforms for one-click prompt transfer
  • Community-contributed scenario templates to expand use case coverage
  • Prompt history and versioning to track what works best over time
  • AI-powered prompt optimization suggestions based on success patterns
  • Collaborative features for teams to share and refine prompt templates
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