Insights, thoughts, and updates on Responsible AI, GenAI governance, and industry best practices from the Corridor GGX team.
With the rise of large language models, there's a growing trend to use them as judges, not just generators. This piece explores LLM-as-a-Judge from first principles, examines its use across evaluation tasks, dives deep into toxicity detection, and applies best practices to create more reliable and robust evaluation systems.
Large Language Models have demonstrated a remarkable ability to generate fluent, coherent, human-like text. Beneath this polished exterior lies a significant challenge - hallucination, where an LLM generates information that is nonsensical, factually incorrect, or unfaithful to a provided source.
Everyone is building AI agents, but there's a big gap between a cool demo and a reliable, production-ready system. Fix one issue, and something else breaks - sometimes badly. We break down how agents work, why evaluating them is essential, and the key evaluation methods needed to move beyond simple "vibe checks."
Risk and compliance teams are increasingly being asked to sign off on customer-facing GenAI. We unpack the evidence trail - applicable risks, standardized evaluations, thresholds, mitigations - that turns one-off testing into a defensible approval.