Giskard launches RealPerformance to address the gap between the focus on security and business compliance issues: the first systematic dataset of business performance failures in conversational AI, based on real-world testing across banks, insurers, and other industries.
Our Phare benchmark reveals that leading LLMs reproduce stereotypes in stories despite recognising bias when asked directly. Analysis of 17 models shows the generation vs discrimination gap.
Enterprise AI teams often treat observability and evaluation as competing priorities, leading to gaps in either technical monitoring or quality assurance.
Enterprise AI teams need both immediate protection and deep quality insights but often treat guardrails and batch evaluations as competing priorities.
Explore how false content is generated by AI and why it's critical to understand LLM vulnerabilities for safer, more ethical AI use.
Discover the key vulnerabilities in Large Language Models (LLMs) and learn how to mitigate AI risks with clear overviews and practical examples. Stay ahead in safe and responsible AI deployment.