Large language models (LLMs) are revolutionizing the way we interact with technology, but they also come with the risk of perpetuating and amplifying societal biases. A new research paper introduces a practical framework for assessing and addressing these biases in real-world applications. LLMs can exhibit bias in several ways, from generating toxic or stereotypical language to making unfair recommendations or classifications. This framework helps navigate the complex landscape of bias detection by providing a decision-making guide for choosing the right metrics for different LLM use cases. The research categorizes different types of bias, including toxicity, stereotyping, counterfactual fairness (how outputs change based on protected attributes like gender or race), and allocational harms (unequal distribution of resources). It then maps these risks to common LLM tasks like text generation, classification, and recommendation. Instead of relying on generic benchmark datasets, the framework encourages evaluating bias within the specific context of an application, using the actual prompts and data the LLM will encounter. This makes the assessment more relevant and actionable. Several new metrics are introduced, including innovative counterfactual metrics that compare LLM outputs generated from slightly altered inputs, and metrics based on stereotype classifiers. By focusing on the LLM's output, the framework makes bias detection accessible even without deep technical knowledge of the model's internal workings. This empowers practitioners to assess and mitigate bias effectively, promoting fairer and more equitable outcomes in AI systems. The framework doesn’t offer a one-size-fits-all solution, recognizing that different use cases demand different approaches. It aims to equip users with the tools to make informed decisions about which biases matter most and how to measure them. While promising, this framework acknowledges the ongoing challenges in bias detection and mitigation. Future research will continue refining these methods and addressing the complex interplay between language, technology, and societal biases.