As an independent AI evaluator and open-source stress-tester, I specialise in the critical identification and documentat
As an independent AI evaluator and open-source stress-tester, I specialise in the critical identification and documentation of edge cases, biases, and logical failures in publicly deployed large language models. Through rigorous, real-world interaction, I systematically probe consumer-facing AI systems to expose vulnerabilities and inaccuracies that bypassed standard training guardrails. This proactive, self-directed data auditing not only demonstrates a deep, practical understanding of prompt engineering and model behaviour, but it also mirrors the essential work of red-teaming and reinforcement learning from human feedback (RLHF) required to build safer, more reliable AI.
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