Answer Relevancy in RAG Evaluation Answer relevancy in Retrieval-Augmented Generation (RAG) systems refers to how well a generated response addresses the user’s query while staying grounded in the retrieved information. A relevant answer must directly answer the question and avoid introducing unrelated or unsupported details. For example, if a user asks, “What causes climate change?” and the RAG system retrieves documents about greenhouse gases, a relevant answer would focus on CO2 emissions, not unrelated topics like renewable energy costs. Relevancy ensures the model doesn’t “hallucinate” or drift away from the provided context.
Measuring Answer Relevancy Relevancy can be measured through automated metrics and human evaluation. Automated approaches include:
- Entailment Checking: Use pre-trained models (e.g., BERT-based classifiers) to verify if the answer is logically supported by the retrieved documents. For instance, if the retrieved context states, “CO2 traps heat,” the answer “CO2 emissions cause global warming” would score highly.
- Keyword/Entity Overlap: Track whether key terms from the retrieved documents appear in the answer. A low overlap might indicate irrelevancy.
- Question-Answer (QA) Correlation: Compute semantic similarity (e.g., using Sentence-BERT) between the answer and the original query to ensure alignment.
Human evaluation remains a gold standard, where annotators rate answers on a scale (e.g., 1-5) for how well they address the query and use retrieved content.
Practical Considerations Developers should combine automated metrics with spot-checks for reliability. For example, a hybrid approach might use entailment scores to filter blatantly irrelevant answers, followed by manual reviews for edge cases. Tools like ROUGE-L or BLEU are less effective here, as they focus on word overlap rather than contextual accuracy. Instead, frameworks like RAGAS (RAG Assessment) provide specialized metrics for relevancy, such as “answer faithfulness” (is the answer supported by context?) and “answer correctness” (does it address the query?). By iteratively testing with diverse queries and refining retrieval/generation components, developers can systematically improve relevancy in RAG systems.