Expert Support case study: Bolstering a RAG app with LLM-as-a-Judge

The article discusses the implementation of an LLM-as-a-Judge architecture to enhance a Retrieval-Augmented Generation (RAG) application, focusing on its ability to evaluate and refine generated outputs based on retrieved context. Key technical details include the integration of a transformer-based LLM that operates in tandem with a retrieval system to provide contextual feedback, improving the accuracy and relevance of generated responses. This approach is significant for practitioners as it demonstrates how leveraging LLMs for judgment can optimize RAG applications, leading to more precise and contextually aware AI solutions.