To test a RAG system's consistency across different phrasings, start by creating a diverse set of semantically equivalent questions. Use paraphrasing techniques (e.g., synonym replacement, structural changes) or tools like GPT-4 to generate variations of core questions. For example, "What causes climate change?" could become "Why does global warming occur?" or "What factors drive changes in Earth's climate?" Pair each variation with a reference answer that defines the expected response. This dataset becomes the foundation for systematic testing.
Next, implement automated evaluation metrics to measure consistency. Use embedding-based similarity scores (e.g., cosine similarity between generated and reference answers) alongside semantic equivalence classifiers like BERTScore or Sentence-BERT. For stricter validation, add rule-based checks for key facts or entities that must appear in all answers. Automated tests should run these variations through the RAG pipeline and flag responses that fall below similarity thresholds or miss critical information. For example, if "greenhouse gases" appears in reference answers but is missing in a generated response, the test fails.
Finally, validate results with human evaluation. Automated metrics can miss nuances, so sample testing with domain experts is crucial. Track metrics like answer correctness, completeness, and phrasing neutrality across question variations. Additionally, test the retrieval component separately by checking if different phrasings retrieve the same relevant document passages. For example, ensure both "How do vaccines work?" and "Explain vaccine mechanisms" pull similar medical literature. Combine these approaches in a continuous testing pipeline to catch regressions during development.