To test a RAG system for consistency across different phrasings of the same question, start by creating a dataset of semantically equivalent questions with varied wording. For example, questions like “How does photosynthesis work?” and “Explain the process of converting sunlight into energy in plants” should retrieve the same core information. Use automated metrics to compare answers, such as embedding similarity (e.g., cosine similarity between answer vectors from models like BERT) or overlap of key terms (e.g., checking for “chlorophyll,” “CO2,” “glucose”). This helps quantify whether answers address the same concepts, even if wording differs. Additionally, design unit tests that flag answers with low similarity scores or missing critical details, allowing developers to identify gaps in retrieval or generation.
Next, evaluate the system’s robustness to structural variations. For instance, test questions with added noise (“Can you tell me, like, how photosynthesis works?”) or reordered clauses (“In plants, what’s the mechanism behind energy production using light?”). These variations assess whether the retrieval component correctly identifies the underlying intent. If the system relies on keyword matching, it might fail when questions omit specific terms. To mitigate this, test retrieval separately by comparing the documents or context snippets fetched for each phrasing. If different phrasings retrieve inconsistent context, fine-tune the retriever or expand synonym coverage in the knowledge base. For the generator, ensure it synthesizes consistent answers even when context varies slightly—e.g., by testing if answers to “What’s the capital of France?” and “Paris is the capital of which country?” both clearly state “Paris” without ambiguity.
Finally, incorporate human evaluation for nuanced cases. For example, if a user asks, “How do I reset my password?” versus "My login isn’t working; how do I recover access?", automated metrics might miss subtle differences in expected steps (e.g., “reset” vs. “recover”). Human reviewers can assess whether answers are functionally equivalent and meet user intent. To scale this, use a sampling strategy: automatically test 80% of variations with metrics and manually audit the remaining 20%. Document common failure modes, such as over-reliance on specific phrasing in retrieval or excessive creativity in generation, and iteratively refine the system. This hybrid approach balances efficiency with thoroughness, ensuring high answer quality across diverse inputs.
