The rise of generative AI (Gen AI) applications is poised to revolutionize the way businesses operate, offering unprecedented capabilities in areas like customer service, content creation, and data analysis. However, the complexity and unpredictability of non-deterministic applications present unique challenges when it comes to testing and evaluation.

In this post, we discuss the rapidly evolving landscape of AI governance and regulation, particularly focusing on Generative AI. We highlight the challenges businesses face in keeping up with global AI policies, with over 1000 AI initiatives from 69 countries, territories, and the EU.

In this blog post, we’ll explore the importance of QA in identifying issues like fairness, robustness, and transparency, and how tailored testing solutions can mitigate risks associated with AI, ensuring your applications deliver consistent, high-quality outcomes in real-world environments.

Learn how to ensure robustness, reliability, and compliance in LLM application testing with these essential strategies and best practices.

In this post, we delve deeper into one crucial aspect of compliance: ethical considerations, specifically focusing on bias and toxicity in LLM applications.