To create a test set for a RAG (Retrieval-Augmented Generation) system, start by leveraging existing question-answering (QA) datasets and augmenting them with context documents and verified answers. Begin with datasets like SQuAD, Natural Questions, or TriviaQA, which already provide questions and answers. These datasets often include source documents (e.g., Wikipedia paragraphs) that can serve as context. For example, SQuAD pairs questions with specific passages containing answers, allowing you to directly map questions to their supporting context. If the dataset lacks explicit context (e.g., open-domain QA datasets), use a retriever model like BM25 or DPR to fetch relevant documents from a knowledge base (e.g., Wikipedia) for each question. Ensure the retrieved context contains the answer or sufficient information to infer it.
Next, validate the alignment between questions, context, and answers. Manually or programmatically check that the answer is present in the provided context. For instance, if the question is “When was the electric guitar invented?” and the context states “Les Paul developed the solid-body electric guitar in 1940,” verify that “1940” is the correct answer. For ambiguous questions, ensure the context resolves ambiguity. If a question asks, “What is Java?” and the context discusses programming, the answer should reference the language, not the island. For multi-hop questions (e.g., “What team did Player X join after leaving Team Y?”), combine multiple context snippets to ensure the answer can be logically derived.
Finally, structure the test set to cover diverse scenarios. Include straightforward fact-based questions, inferential questions requiring reasoning (e.g., calculating ages from birth years), and questions with multiple valid answers (e.g., synonyms like “automobile” vs. “car”). Split the dataset into training, validation, and test subsets to avoid overlap and ensure fair evaluation. Tools like Hugging Face Datasets or custom scripts can automate context retrieval and alignment checks. For example, using the Haystack framework, you could index Wikipedia, retrieve top-k documents for each question, and filter out irrelevant contexts. This approach balances efficiency and rigor, providing a reliable benchmark for evaluating RAG’s retrieval and generation capabilities.
