How do you build a RAG evaluation dataset for your specific domain?

Building a domain-specific RAG evaluation dataset requires creating question-context-answer triples that represent realistic user queries. Start by collecting actual user questions from search logs, support tickets, and stakeholder interviews. For each question, identify the ground truth source passages that contain the answer. Write reference answers grounded in those passages. Categorize questions by type including factual lookup, multi-hop reasoning, comparison, summarization, and procedural. Include adversarial queries that test for hallucination such as questions not answerable from the knowledge base. Include edge cases like questions about topics at the boundary of coverage. Create at least 100-200 evaluation examples with balanced representation across categories and difficulty levels. Have domain experts validate the reference answers. Version the evaluation set and expand it over time. Use this dataset to benchmark changes to chunking, embedding, retrieval, and generation components with quantitative metrics ensuring that improvements in one area do not degrade others.