As artificial intelligence agents become more powerful, agentic AI governance becomes increasingly important – and yet, today ...
The offline pipeline's primary objective is regression testing — identifying failures, drift, and latency before production. Deploying an enterprise LLM feature without a gating offline evaluation ...
Organizations need to internalize a simple principle: Calling an LLM API is a data transfer. You're trusting the provider ...
Kumar, Aounon, Chirag Agarwal, Suraj Srinivas, Aaron Jiaxun Li, Soheil Feizi, and Himabindu Lakkaraju. "Certifying LLM Safety Against Adversarial Prompting." Conference on Language Modeling (2024).
New tools for filtering malicious prompts, detecting ungrounded outputs, and evaluating the safety of models will make generative AI safer to use. Both extremely promising and extremely risky, ...
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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More With 77% of enterprises already victimized by adversarial AI attacks and ...
Companies investing in generative AI find that testing and quality assurance are two of the most critical areas for improvement. Here are four strategies for testing LLMs embedded in generative AI ...
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