Edge cases in generative AI refer to rare occurrences where models produce irrelevant or biased output due to incorrect assumptions. To handle these cases, businesses prioritize edge case handling in their AI development strategies. AI systems require human supervision to identify and address areas where models get stuck or make incorrect decisions. This involves a balance of unsupervised and supervised learning, with regulatory guardrails to address harmful biases and ensure sensitivity when tackling sensitive topics.