Large Language Models (LLM) have become a popular approach for implementing Retrieval Augmented Generation (RAG) systems, and a significant amount of effort has been spent on building good models and metrics. In spite of increased recognition of the need for rigorous evaluation of RAG systems, few tools exist that go beyond the creation of model output and automatic calculation. We present InspectorRAGet, an introspection platform for performing a comprehensive analysis of the quality of RAG system output. InspectorRAGet allows the user to analyze aggregate and instance-level performance of RAG systems, using both human and algorithmicmetrics as well as annotator quality. InspectorRAGet is suitable for multiple use cases and is available publicly to the community.A live instance of the platform is available at https://ibm.biz/InspectorRAGet