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David Berenstein
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RealPerformance, A Dataset of Language Model Business Compliance Issues
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.
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LLMs recognise bias but also reproduce harmful stereotypes: an analysis of bias in leading LLMs
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.
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LLM Observability vs LLM Evaluation: Building Comprehensive Enterprise AI Testing Strategies
Enterprise AI teams often treat observability and evaluation as competing priorities, leading to gaps in either technical monitoring or quality assurance.
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Real-Time Guardrails vs Batch LLM Evaluations: A Comprehensive AI Testing Strategy
Enterprise AI teams need both immediate protection and deep quality insights but often treat guardrails and batch evaluations as competing priorities.
A Practical Guide to LLM Hallucinations and Misinformation Detection
Explore how false content is generated by AI and why it's critical to understand LLM vulnerabilities for safer, more ethical AI use.
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A Practical Guide on AI Security and LLM Vulnerabilities
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.
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