Llama Datasets, introduced by Andrei Fajardo and Jerry Liu at LlamaIndex, are a collection of community-contributed datasets designed to facilitate benchmarking for Retrieval-Augmented Generation (RAG) pipelines across various use cases. These datasets include question-answer pairs and source context, available for download from LlamaHub, and can be evaluated using a set of metrics. The initiative addresses the challenge of evaluating LLM systems, which are stochastic and difficult to test with traditional unit tests, by offering datasets tailored to specific production use cases. Initially launching with ten datasets, Llama Datasets aims to provide flexibility by allowing users to choose appropriate datasets for their needs while also enabling easy contributions from users, who can upload their own datasets by submitting data cards and raw files to LlamaHub. The project includes tools like the RagEvaluatorPack to assist in performance measurement across various metrics and encourages user contributions to expand the dataset offerings.