Retrieval-Augmented Generation (RAG) systems rely on retrieving answers from a knowledge base before generating responses. To evaluate them effectively, you need a test dataset that reflects what the system should know. Instead of manually creating test cases, you can generate them directly from your knowledge source, ensuring accurate and relevant ground truth data.

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Create a RAG test dataset

You can generate ground truth RAG dataset from your data source.

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1. Create a Project

In the Evidently UI, start a new Project or open an existing one.
  • Navigate to “Datasets” in the left menu.
  • Click “Generate” and select the “RAG” option.

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2. Upload your knowledge base

Select a file containing the information your AI system retrieves from. Supported formats: Markdown (.md), CSV, TXT, PDFs. Choose how many inputs to generate. Simply drop the file, then:
  • Choose the number of inputs to generate.
  • Choose if you want to include the context used to generate the answer.
The system automatically extracts relevant facts and generates user-like questions to your data source with ground truth answers.
Note that it may take some time to process the dataset. Limits apply on the free plan.

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3. Review the test cases

You can preview and refine the generated dataset. You can:
  • Use “More like this” to add more variations.
  • Drop rows that aren’t relevant.
  • Manually edit questions or responses.

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4. Save the Dataset

Once you are finished, store the dataset. You can download it as a CSV file or access it via the Python API using the dataset ID to use in your evaluation.
Dataset API. How to work with Evidently datasets.