According to Statista, the AI market size is projected to rise from 241.8 billion U.S. dollars in 2023 to almost 740 billion U.S. dollars in 2030, accounting for a compound annual growth rate of 17.3%.
Releasing a polished AI generative model requires thorough development and precise planning. Simply put, the quality of the end product will be highly dependable on the quality of testing conducted throughout the development lifecycle. That is why we created a list of top generative AI testing tools that you can use to increase the overall quality of your AI model. Let’s begin!
5 Generative AI testing tools
1. Global App Testing – “Get your product to market faster with best-in-class Generative AI testing”
While we understand the importance of prioritizing ourselves, we kindly ask that you consider including Global App Testing on your list of potential suppliers for testing generative AI platforms.
Specializing in crowdtesting, we offer a variety of testing services for all stages of Generative AI development to assist developers in identifying bugs and issues in their platform. With a diverse community of over 90,000 testers worldwide, we can perform tests on actual devices and software environments in more than 190 countries and territories.
With our platform, you can efficiently assign test cases and exploratory tests and receive results within a rapid 6-48 hour timeframe.
Key features
Besides various testing types for mobile, web, and IoT, here is what we can assist you with in terms of Generative AI testing:
- Content guideline compliance: Check generated content against guidelines to identify false, inappropriate, or uncanny outputs.
- Red team testing: Use professional red teams to simulate bad-faith user behavior and protect against damaging use.
- Bias assessment: Conduct surveys targeting specific demographics to assess perceived content bias.
- UX and UI testing: Apply traditional QA and UX testing tools to ensure a perfect user experience and interface functionality.
- Device compatibility and accessibility: Verify your AI product’s functionality across different devices and ensure accessibility compliance.
- AI Act compliance verification: Test features developed for compliance with real users and devices, providing a full breakdown of functionality.
- Exploratory and scenario-based testing: Simulate real-world environments and explore the product for potential guideline violations.
- Expert consultation and best practices: Access a GenAI safety and quality primer with best practices, tools, and resources for developing safe, high-quality AI products.
2. Applause – “Optimize the benefits of generative AI while ensuring accuracy and relevance.”
Applause is a digital quality assurance company that offers a range of testing services for web and mobile applications and other various platforms. These include functional testing, usability testing, payment testing, load testing, security testing, and localization testing.
The company also has a large community of professional testers from around the world who can provide feedback and insights on an application's user experience.
Key features
Some of the key features for Generative AI testing highlighted on the Applause website are:
- Custom testing plans: Develop and execute flexible, project-specific testing frameworks tailored to your generative AI needs.
- Personalized recommendations evaluation: Ensure AI-generated recommendations are relevant and aligned with individual user preferences.
- Content quality assessment: Assess generative content's quality, uniqueness, and user engagement to meet desired objectives.
- NLP model validation: Validate the accuracy, coherence, and appropriateness of generative NLP models for effective user interaction.
- Comprehensive validation testing: Conduct extensive validation testing to cover various scenarios, inputs, edge cases, and potential failure points.
- Continuous monitoring and real-world simulation: Perform ongoing validation and testing in real-world environments to maintain model performance, detect bias, and ensure robust functionality over time.
3. QA Wolf – “Automated end-to-end testing for Gen AI & LLMs”
QA Wolf is an open-source framework for end-to-end tests. It is specifically designed for modern web apps and integrates seamlessly with your existing development workflow. QA Wolf simplifies the process of writing and running tests, making it easier for teams to ensure the quality and performance of their web applications.
Key features
Some of the key features highlighted on the QA Wolf website are:
- Specialized back box testing: Expertise in black box testing for generative AI and LLMs, replicating tests thousands of times with non-deterministic assertions.
- Rapid release: Ability to run thousands or millions of tests in parallel, providing consistent feedback on model performance in real-world scenarios within minutes.
- Bias detection and prevention: By adhering to emerging standards and regulations, measure and prevent bias in generated content.
- Performance and concurrency measurement: Ensure UI and APIs can handle concurrent requests, measuring latency and successful response completion.
- Context retention testing: Validate the AI’s ability to maintain and use in-session memory for better user engagement and commercial viability.
- Integration testing: Test and validate connections with external services, APIs, and databases to ensure seamless data ingestion and usage.
4. Gartner – “What Generative AI Means for Business”
Gartner is a global research and advisory company known for providing insights, advice, and tools to help businesses make informed decisions in various areas, including technology, finance, and marketing. Their expertise spans across multiple industries, and they are well-regarded for their research reports, market analysis, and consulting services. Even though they do not offer generative AI testing, they provide various services that can help you elevate your AI product.
Key features
Some of the key features highlighted on the Gartner website that are GenAI-related are:
- An executive guide to GenAI: Understand trends and technologies in Generative AI, pilot initiatives, and future scope for business impact.
- GenAI strategy planner: Focus on feasible and valuable GenAI initiatives with a downloadable workbook for strategic planning.
- AI opportunity radar: Use frameworks to vet and prioritize GenAI use cases based on business value and feasibility.
- Risk management: Address risks like transparency, accuracy, hallucinations, bias, IP exposure, cyber attacks, and sustainability impacts.
- Pilot implementation steps: Generate and prioritize use-case ideas, form a fusion team, design and plan the pilot, deliver, and iterate.
- Major GenAI technologies: AI foundation models, prediction algorithms, and domain-specific models built on top of foundation models.
5. MobiDev – “Artificial Intelligence Development Services”
MobiDev is a full-service software development company specializing in custom software solutions for businesses of all sizes. With a focus on innovation and cutting-edge technology, Mobidev offers services such as web development, mobile app development, UI/UX design, and cloud solutions. Their team of skilled professionals works closely with clients to deliver tailor-made software products that meet specific business needs and goals.
Key features
Some of the key features highlighted on the Digivante website are:
- Research-based approach: Identify the best AI solutions for your ideas, mitigating project risks and saving time and money.
- AI in finance & banking: Improve decision-making accuracy, reduce risks, and enhance financial operations.
- AI for healthcare: Utilize AI for early disease detection, treatment, and workspace automation to improve patient outcomes.
- AI for security systems: Enhance security by minimizing risks, improving authentication mechanisms, and responding quickly to cybersecurity threats.
- AI for retail: Boost customer experience, streamline operations, and increase sales with intelligent software solutions.
- AI for marketing: Drive personalization, optimize marketing campaigns, and maintain customer loyalty through AI-driven strategies.
- AI for logistics & supply chain: Reduce production and logistics delays, increase space efficiency, optimize planning, and improve quality control.
Conclusion
Ensuring effective generative AI testing presents a unique set of challenges, with potential issues often being overlooked. However, you can significantly enhance the testing process by utilizing some of the mentioned generative AI testing tools. These tools improve the accuracy and reliability of your AI models, leading to increased user trust and satisfaction, ultimately driving significant advancements in your AI capabilities.
Why is Global App Testing a good choice for Generative AI testing?
Global App Testing is a crowdsourced testing platform with a service specifically crafted for Generative AI testing. With over 90,000 real testers in 190+ countries, Global App Testing assists with real-user testing, global coverage, and compliance-focused assessments that can be customized to your needs.
Our Generative AI testing additionally includes:
- Comprehensive best practices primer: Access our detailed guide featuring the best practices for GenAI safety, quality, and compliance, along with links to essential tools and resources to help businesses build robust and future-ready AI processes.
- Red team testing: Protect against bad-faith user behavior by employing professional red teams to identify and mitigate inappropriate, false, or offensive content, ensuring the integrity of your AI product.
- User experience (UX) evaluation: Enhance the overall user experience of your generative AI product by applying traditional QA and UX testing tools to identify and resolve UI and interface issues, ensure device compatibility, and improve accessibility.
- Performance under diverse conditions: Test your generative AI models in various real-world environments to simulate user interactions and environmental conditions, ensuring robust performance and reliability across different scenarios.
- Continuous monitoring and improvement: Implement ongoing validation testing and monitoring services to detect and address any performance degradation or issues over time, maintaining the effectiveness and accuracy of your generative AI models.
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