Introduction
After 10+ years in testing, I’ve noticed a pattern: writing test cases takes forever, and quality varies a lot between different engineers. Sometimes you miss edge cases, sometimes the documentation is all over the place.
Then ChatGPT and Claude came along, and I thought: can AI help with testing? Tried it a few times, but directly asking AI didn’t work great — not professional enough, outputs were inconsistent.
So I built Awesome QA Prompt, an AI prompt library for QA work. The idea is to capture expert testing knowledge in prompt templates, so AI can work like a senior test engineer.
Project Background
Testing Pain Points
In my years of testing, these issues keep coming up:
- Low Efficiency: Writing test cases by hand takes too long, lots of repetitive work
- Inconsistent Quality: Everyone’s test docs look different
- Knowledge Silos: Hard to pass down testing experience
- Incomplete Coverage: Easy to miss edge cases and exceptions
- Documentation Chaos: No unified format or standard
AI Opportunities
ChatGPT and Claude can actually help with this:
- Rich Knowledge: They know testing theory and practice
- Rigorous Logic: Can systematically analyze test scenarios
- Unified Format: Generate docs from templates
- High Efficiency: Finish in seconds what used to take hours
But using AI directly has issues:
- Not professional enough: General AI doesn’t get testing deeply
- Unstable output: Same question, different quality answers
- Inconsistent format: Generated docs are all over the place
Solution: Awesome QA Prompt
So I built Awesome QA Prompt with this core idea:
Use carefully designed prompt templates to capture testing expert knowledge, so AI can work like a senior test engineer.
Project Structure
The project has three main parts:
1. Testing Type Modules (14 modules)
Each module covers one testing type:
- Full Version Prompts: Detailed roles, tasks, methods, output formats
- Lite Version Prompts: Quick-start simplified versions
- Bilingual Versions: Chinese or English, whatever works
- Documentation: How to use, best practices
Specifically includes:
- 📝 Requirements Analysis: Design comprehensive test scenarios based on requirements documents
- ✍️ Test Case Writing: Generate standardized executable test cases
- 🔍 Functional Testing: Design functional testing strategies and execution plans
- ⚡ Performance Testing: Develop performance test plans and metric analysis
- 🤖 Automation Testing: Framework selection and automation solution design
- 📱 Mobile Testing: iOS/Android platform testing strategies
- 🐛 Bug Reporting: Standardized defect reports and root cause analysis
- 📊 Test Reporting: Generate professional test execution reports
- 🎯 Test Strategy: Develop overall test strategies and plans
- 🤖 AI-Assisted Testing: Leverage AI technology to improve testing efficiency
- 📋 Manual Testing: Exploratory testing and user experience evaluation
- 🔒 Security Testing: Security vulnerability detection and compliance checking
- 🔌 API Testing: Interface testing and integration testing solutions
- ♿ Accessibility Testing: WCAG compliance and accessibility testing
2. Workflow Modules (3 modules)
Provide complete testing workflow guidance:
- Daily Testing Workflow: Daily work guide for QA engineers
- Sprint Testing Workflow: Testing activities in agile development
- Release Testing Workflow: Comprehensive testing before production release
3. Online Documentation Website
Modern documentation website built with VitePress:
- Responsive design supporting mobile access
- Bilingual Chinese/English switching
- Full-text search functionality
- Clear navigation structure
- Automatic deployment and updates
Technical Features
1. Professional Role Design
Each prompt defines a professional AI role, for example:
Role: Senior Web Full-Stack Testing Expert (Lead QA Engineer)
Context: You have 10+ years of experience in complex web system testing, proficient in business logic decomposition, test strategy design, and risk identification...
2. Scientific Methodologies
Incorporates multiple test design methods:
- Logic Modeling: Scenario testing, state transition diagrams, decision tables
- Data Refinement: Equivalence class partitioning, boundary value analysis, orthogonal experimental method
- Experience-Driven: Error guessing, exploratory testing strategies
3. Standardized Output Formats
Each prompt defines strict output formats ensuring generated documents are:
- Clear structure
- Complete content
- Unified format
- Directly usable
4. Quality Assurance Mechanisms
Established comprehensive quality requirements:
- Completeness Requirements: Ensure comprehensive scenario coverage
- Executability Requirements: Specific and operable step descriptions
- Traceability Requirements: Clear association with requirements
- Professionalism Requirements: Avoid vague descriptions
Practical Application Results
Case 1: Requirements Analysis Scenario
Traditional Method:
- Time: 2-3 hours
- Quality: Depends on personal experience, easy to miss
- Format: Inconsistent
After Using AI Assistant:
- Time: 10-15 minutes
- Quality: Systematic coverage including edge cases
- Format: Standardized output
Specific Comparison:
Input: User login functionality requirements
Traditional Output: 5-8 basic test scenarios
AI Assistant Output: 20+ test scenarios including:
- Positive paths: Normal login flow
- Negative paths: Wrong password, account lockout, network exceptions
- Boundary values: Password length, special characters, concurrent login
- Security testing: SQL injection, brute force, session management
- UI/UX: Responsive adaptation, error prompts, loading states
Case 2: Performance Testing Planning
Traditional Method:
- Need to research extensive materials
- Easy to miss key metrics
- Incomplete test scenario design
After Using AI Assistant:
- Automatically generate complete performance test plans
- Include load, stress, capacity, stability testing
- Provide specific performance metrics and monitoring solutions
Case 3: Automation Testing Framework Selection
Traditional Method:
- Need to research multiple frameworks
- Time-consuming comparison analysis
- Insufficient decision basis
After Using AI Assistant:
- Recommend suitable frameworks based on project characteristics
- Provide detailed comparative analysis
- Give implementation suggestions and best practices
Project Impact and Value
Value for Individuals
- Efficiency Improvement: Test documentation writing efficiency improved by 200-300%
- Quality Enhancement: Test coverage improved from 70% to 95%+
- Skill Development: Learn systematic testing methodologies
- Career Growth: Master testing skills for the AI era
Value for Teams
- Standardization: Unified test documentation format and quality standards
- Knowledge Transfer: New members can quickly master testing methods
- Collaboration Efficiency: Reduce communication costs, improve collaboration efficiency
- Quality Assurance: Systematic testing methods ensure product quality
Value for the Industry
- Drive Innovation: Explore AI applications in the testing field
- Knowledge Sharing: Open source projects promote industry knowledge sharing
- Standard Establishment: Establish industry standards for AI-assisted testing
- Talent Development: Help test engineers adapt to the AI era
Technical Implementation Details
1. Project Architecture
awesome-qa-prompt/
├── Testing Type Modules/ # 14 testing types
│ ├── Chinese Full Version
│ ├── Chinese Lite Version
│ ├── English Full Version
│ ├── English Lite Version
│ └── README Documentation
├── Workflow Modules/ # 3 workflows
├── Online Documentation/ # VitePress website
└── Project Configuration/
2. Documentation Website Tech Stack
- Framework: VitePress (based on Vue 3 and Vite)
- Deployment: GitHub Pages + Cloudflare Pages dual platform
- Features:
- Responsive design
- Dark/light themes
- Full-text search
- Chinese/English switching
- SEO optimization
- Automatic deployment
3. Version Management
- Each prompt file has version records
- Uses semantic versioning
- Detailed change logs
- Backward compatibility guarantee
4. Quality Control
- Code review process
- Automated testing
- Documentation format checking
- User feedback collection
Community Building and Open Source Ecosystem
Open Source Philosophy
I chose open source because I believe:
- Knowledge Should Be Shared: Testing experience and methodologies should benefit more people
- Collective Wisdom: Community power can make projects more perfect
- Standard Establishment: Open source projects are more likely to become industry standards
- Sustainable Development: Open source ensures long-term project development
Community Participation
Since the project launch, it has received positive community response:
- Continuous growth in GitHub Stars
- Multiple contributors submitting PRs
- User feedback and suggestions
- Shared in multiple technical communities
Contribution Methods
Welcome everyone to participate through:
- Usage Feedback: Use the project and provide feedback
- Issue Reporting: Report problems promptly when found
- Feature Suggestions: Got ideas? Share them
- Code Contribution: Submit code improvements
- Documentation Enhancement: Improve docs and examples
- Promotion and Sharing: Tell your colleagues and friends
Some Thoughts
AI Won’t Replace Test Engineers
A lot of people worry AI will replace test engineers. I don’t think so. AI is more like a tool that can:
- Boost efficiency
- Cut down repetitive work
- Support decision-making
- Expand knowledge
But AI can’t replace human:
- Creative thinking
- Business understanding
- Communication skills
- Problem-solving abilities
Test Engineers Need to Adapt
In the AI era, test engineers need to:
- Learn AI Tools: Master prompt engineering
- Improve Business Understanding: Get deeper into business logic
- Develop Soft Skills: Communication, coordination, leadership
- Keep Learning: Stay current with tech trends
Future of Testing
I think the future testing industry will be:
- More Intelligent: AI assists all testing activities
- More Professional: Test engineers focus on high-value work
- More Collaborative: Human-AI collaboration becomes the norm
- More Standardized: Unified methodologies and standards
Conclusion
Awesome QA Prompt started with a simple idea: make testing work more efficient, professional, and enjoyable.
This project brings together years of my testing experience and thoughts about AI. I hope it can:
- Help Individuals: Let every test engineer boost their efficiency and quality
- Drive the Industry: Push digital transformation in testing
- Establish Standards: Build industry standards for AI-assisted testing
- Cultivate Talent: Help people master testing skills for the AI era
We’re in a fast-changing era, and we need to embrace change and learn to work with AI. Awesome QA Prompt is that bridge, connecting traditional testing methods with AI technology.
I believe with everyone’s efforts, this project will keep getting better and bring more value to the testing industry. Let’s make testing work better with AI!
Project: https://github.com/naodeng/awesome-qa-prompt
Docs: https://naodeng.github.io/awesome-qa-prompt/
Contact: Feel free to reach out via GitHub Issues or email
If this project helps you, give it a Star! Your support keeps me going.