RAG-Mini-Wikipedia
RAG-Mini-Wikipedia bundles two parts: a set of 918 questions with reference answers, and a corpus of roughly 3,200 short passages drawn from Wikipedia. The passages are already split into retrieval-sized chunks, so you can load them straight into a vector database and start measuring how well your system finds the right context.
Because it is compact, it is well suited to fast iteration. You can run a full evaluation in minutes, compare retrieval and answer quality across changes, and keep the whole thing in memory. It is published on Hugging Face in Parquet format under a CC BY 3.0 licence.
Treat it as a sanity-check benchmark rather than a definitive measure. It is ideal for smoke-testing a pipeline or teaching the basics of RAG evaluation, then moving to larger benchmarks once your setup works.
Related sources
FlashRAG Benchmark Datasets
A research toolkit that bundles standardised, pre-processed benchmark datasets for RAG evaluation across many scenarios. Every dataset comes in a single unified JSONL format, so you can swap benchmarks in and out without rewriting your data loading each time.
Open RAGBench (Vectara)
A RAG evaluation benchmark from Vectara built on 1,000 arXiv papers, with multimodal content extraction and 3,045 question-and-answer pairs across scientific domains. Designed to test retrieval and answer quality on real research documents rather than short, simplified passages.
PleIAs RAG-Resources
A curated collection of datasets and resources assembled by PleIAs for building and evaluating RAG applications. A useful starting point when you want ready-made material to test retrieval pipelines without hunting for datasets one by one.