Your Severity Weights Are Made Up (And That's the Problem)

This story was originally published on HackerNoon at: https://hackernoon.com/your-severity-weights-are-made-up-and-thats-the-problem.
Most teams weight LLM hallucination types by gut feel or not at all. Here's a framework for deriving severity weights that actually hold up.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #machine-learning, #ai-hallucinations, #llm-hallucination, #ai-weights, #ai-equal-weighting, #hallucination-rate, #binary-hallucination, #hackernoon-top-story, and more.

This story was written by: @praveenmyakala. Learn more about this writer by checking @praveenmyakala's about page, and for more stories, please visit hackernoon.com.

Most teams either treat every hallucination type as equally bad or assign severity by gut feel in a Slack thread. Both are made up. Real severity weights come from four things: downstream cost, reversibility, detectability, and frequency under load.