Large Language Models (LLMs) have achieved remarkable feats, generating human-quality text and performing a variety of tasks. However, these powerful tools are not immune to the biases present in the data they are trained on. This highlights a critical challenge: ensuring that LLMs offer equitable and fair answers, regardless of the user's backgrou