What the Swiss Cheese Model teaches us about governing GenAI systems
A production GenAI pipeline is a stack of layers, and every layer has holes. Using James Reason's Swiss Cheese Model, we map where risk enters a GenAI system, why business and risk teams see different gaps, and how to catch failures before the holes line up.
Creating Toxicity Detection Using LLM-as-a-Judge: A Guide and Best Practices
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.
Hallucinations in AI: Understanding and Detecting Them
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.
Beyond "Vibe Checks": A Practical Guide to Evaluating Agents
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."