Many voices, one reward: multi-role rubrics for LLMs

The evaluation of large language models (LLMs) has become a central challenge for companies seeking to integrate artificial intelligence into their processes. Traditional methods, based on a single generic evaluator, often overlook critical dimensions of human judgment, known as 'dimensional blind spots.' To overcome this limitation, approaches such as multi-role rubric generation emerge, involving multiple complementary perspectives to obtain more comprehensive and auditable evaluation criteria. This technique, similar to how in the development of custom applications different viewpoints (user, business, technical) are considered, allows building more robust reward systems for reinforcement learning of LLMs. In the business environment, having reliable evaluation is key to deploying AI agents that interact with customers or automate processes, as it ensures coherent responses aligned with business objectives. Q2BSTUDIO, as a company specialized in artificial intelligence for businesses, integrates these principles into its custom software solutions, offering cybersecurity services and AWS and Azure cloud services that ensure the necessary infrastructure for advanced projects. Additionally, we combine the power of LLMs with business intelligence tools such as Power BI, enabling organizations to extract value from their data and optimize decision-making. The key is not to settle for superficial evaluations: implementing multi-role rubrics within AI workflows is a step toward fairer, more accurate systems adaptable to each company's real needs.