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