To detect if a RAG system’s answer is factually correct but incomplete or insufficiently detailed, focus on comparing the answer against the retrieved source content to identify gaps. Here’s a structured approach:
Cross-Reference Key Points from Sources Extract key entities, facts, or claims from the retrieved documents using techniques like named entity recognition (NER), keyword extraction, or topic modeling. For example, if a source lists three causes of an event but the answer only mentions two, flag the missing cause. Tools like TF-IDF or BERT-based embeddings can help identify important terms or concepts in the source material. Automate this by generating a checklist of critical points from the sources and verifying their presence in the answer. If the answer omits a significant portion of these points (e.g., skipping a step in a process described in the source), it indicates incompleteness.
Analyze Semantic Coverage Use semantic similarity metrics (e.g., cosine similarity between sentence embeddings) to compare the answer’s content to the source material. While exact matches aren’t necessary, low similarity scores for specific sections of the source could signal missing details. For instance, if the source explains a concept with technical examples but the answer provides only a high-level summary, embeddings might reveal the gap. Additionally, fine-tune a model to score the answer’s comprehensiveness by training it to recognize when answers fail to address sub-topics or nuances explicitly covered in the sources.
Validate with User Feedback or Automated Queries Implement a feedback loop where users rate answer completeness or ask follow-up questions, which can highlight recurring gaps. Alternatively, generate follow-up questions programmatically from the source material (e.g., “What are the three causes mentioned in the document?”) and test whether the RAG answer addresses them. For example, if the source discusses “security, cost, and scalability” as factors, but the answer only covers “security and cost,” automated validation would detect the missing “scalability” detail.
By combining automated checks (key point extraction, semantic analysis) with user-driven feedback, developers can systematically identify answers that lack depth or omit relevant source content, even if they’re factually correct.