The need to align Large Language Models with human expectations introduces significant challenges that extend beyond simple accuracy and relevance. A key difficulty is ensuring that model outputs are ethically sound and non-discriminatory, which involves actively preventing the generation of harmful content and mitigating biases learned from training data.