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Benchmarking Large Language Models (LLMs) is essential for understanding how well a model performs compared to alternatives or prior versions. Effective benchmarking provides organizations with critical insights into the strengths, weaknesses, and readiness of a model for deployment. Performance benchmarking typically begins by defining key metrics including accuracy, robustness, fairness, safety, consistency, efficiency, and explainability. […]

The EU AI Act, along with other emerging global AI regulations, establishes clear requirements for auditing high-risk AI systems such as Large Language Models (LLMs). A robust audit process not only supports regulatory compliance but also mitigates organizational risk and enhances stakeholder trust. The foundation of any audit process starts with clearly defining the intended […]

Measuring the performance and safety of Large Language Models (LLMs) involves more than accuracy. As organizations increasingly deploy LLMs for sensitive applications, a comprehensive evaluation framework becomes essential. Accuracy measures how closely the model’s outputs match the expected ground truth. For example, in legal document review or medical coding, even minor inaccuracies can have major […]

Large Language Models (LLMs) such as OpenAI’s GPT, Meta’s LLaMA, and Google DeepMind’s Gemini are transforming industries by generating human-like text, summarizing data, and assisting in decision-making processes. Despite these advances, LLMs introduce serious risks if deployed without proper oversight. Errors in generated outputs can mislead users, propagate biases, and expose organizations to legal, ethical, […]