Generative AI Testing

Leveraging global real-world data can help you refine your LLMs for more natural, seamless Gen AI experiences. Applause can help you build out high-quality, diverse datasets with a community of experts and generalists spanning domains like writing, mathematics, law or medicine. We can train models with new, task-specific data to improve outputs for your specific use cases. With a vast network of end users and devices within reach, at your direction, we can collect data from thousands of people, including text, images, audio, video, communications (messages and documentation) and biometrics (facial and eye tracking, hand movement) to help ensure optimal results.

After fine-tuning, we leverage human expertise to analyze and enhance Gen AI model and agent performance. As part of a systematic approach to evaluating the accuracy and quality of Gen AI responses to specific prompts, domain experts and end users examine Gen AI responses against multiple dimensions, such as accuracy, completeness, harmfulness, tone, context and more. Grading responses helps identify patterns of issues and areas of improvement in order to deliver a more human experience.

As part of our comprehensive approach, we employ red teaming, an AI best practice that exposes potential vulnerabilities to threats, including bias, racism and other safety risks through adversarial testing. With red team engagements, at your direction, Applause assembles diverse teams to “launch attacks” and uncover issues, ensuring the client has a broad, effective defense system against harmful AI outputs. We also manage trusted tester programs to conduct risk assessments on use case-specific and general applications. Practices like red teaming are critical, particularly with the deployment of broader agentic AI systems which significantly raise the stakes.

Optimize your models and apps with unparalleled access to end-user perspectives from around the globe. Spanning age, gender, ethnicity, disability, sexual orientation, socioeconomic level and more, our testing experts and end users ensure your AI-powered experiences, apps and devices are functional, intuitive and inclusive in every environment. We utilize traditional QA strategies, including exploratory testing for localization and accessibility, as well as user feedback surveys and prompt/output evaluation to test for bias and inaccuracy.

To ensure AI-powered experiences, apps and devices are seamlessly usable, useful and enjoyable, we leverage our deep user experience research and testing capabilities that are ingrained in all of our solutions. Applause manages the strategy and execution, which may include exploratory research, UX studies, longitudinal studies, benchmarking studies, inclusive design or other methodologies. Applause analyzes participants’ feedback and prepares a report with findings and recommendations on how to improve the user experience and ensure it’s compelling and engaging.