How can we detect if a RAG system’s answer, while factually correct, might be incomplete or not sufficiently detailed? (Does it leave out relevant info that was in the sources?)

To detect whether a RAG system’s answer is factually correct but incomplete or lacking detail, developers should focus on comparing the system’s output against the source documents it retrieved. A key step is to analyze whether the answer omits critical information that exists in the sources, even if what’s included is accurate. For example, if a user asks, “What are the causes of climate change?” and the RAG answer correctly lists greenhouse gases but fails to mention deforestation (which is in the sources), this gap indicates incompleteness. To automate this, you could implement checks that measure the overlap between the answer and the source content, such as using keyword matching, entity extraction, or semantic similarity scores (e.g., cosine similarity between answer embeddings and source text embeddings). If the answer lacks terms or concepts explicitly present in the sources, it’s a sign of missing information.

Another approach is to validate the answer’s depth. For instance, if a source document explains a technical process in three steps but the RAG answer summarizes only one, the answer is incomplete. Developers can create test cases where answers are scored against predefined criteria, like the number of key points covered. Tools like BERTScore or ROUGE can quantify how well the answer aligns with source content. However, these metrics aren’t perfect—they might miss nuanced omissions. A practical workaround is to design a rule-based layer that flags answers below a certain length or complexity threshold for manual review. For example, if a user asks for a detailed explanation of neural networks and the RAG response is two sentences despite sources containing paragraphs, the system should flag it as potentially incomplete.

Finally, integrating human-in-the-loop validation or user feedback mechanisms can help identify systematic gaps. For instance, if users consistently ask follow-up questions on the same topic, it suggests the initial answers lack sufficient detail. Developers can also log cases where the RAG system retrieves multiple relevant sources but the answer synthesizes only a subset. For example, if a medical query retrieves five studies on treatment options but the answer mentions only two, this discrepancy highlights incompleteness. Regularly auditing the system’s outputs against source documents and refining retrieval or summarization parameters (e.g., increasing the number of retrieved passages) can mitigate these issues over time.