What does “answer relevancy” mean in the context of RAG evaluation, and how can it be measured? (Consider metrics or evaluations that check if the answer stays on topic and uses the retrieved info.)

Answer Relevancy in RAG Evaluation Answer relevancy in Retrieval-Augmented Generation (RAG) refers to how well a generated answer addresses the user’s query while staying grounded in the retrieved information. A relevant answer must directly respond to the question, avoid unnecessary tangents, and accurately use the facts or context provided by the retrieved documents. For example, if a user asks, “How does Python’s Global Interpreter Lock (GIL) affect multithreading?” a relevant answer would explain the GIL’s role in thread synchronization, cite its impact on CPU-bound tasks, and reference technical documentation or articles used during retrieval. Irrelevant answers might discuss unrelated Python features or fail to connect the GIL to threading limitations.

Measuring Relevancy: Metrics and Methods Relevancy can be measured using automated metrics and human evaluation. A common approach is to use Natural Language Inference (NLI) models to check if the generated answer logically entails (supports) the retrieved context and contradicts off-topic claims. For instance, an NLI model could flag an answer that claims “The GIL improves multithreading performance” if the retrieved documents state the opposite. Overlap-based metrics like BLEU or ROUGE compare the answer’s content to ground-truth references, but they may miss contextual alignment. Alternatively, developers can compute context utilization scores by checking if key terms from the retrieved documents (e.g., “thread-safe,” “CPU-bound”) appear in the answer. Human evaluation remains critical, where reviewers rate answers on criteria like “on-topic focus” and “use of sources” using Likert scales.

Practical Implementation To implement relevancy checks, developers can integrate evaluation pipelines into RAG systems. For example, a Python script using Hugging Face’s transformers library could apply an NLI model to score answers against retrieved contexts. Tools like RAGAS (RAG Assessment) or LlamaIndex’s evaluation modules automate relevancy scoring by combining semantic similarity and keyword-based checks. A practical workflow might:

1. Retrieve documents relevant to the query.
2. Generate an answer using an LLM.
3. Use NLI to verify if the answer aligns with the retrieved context.
4. Flag answers with low entailment scores for review. By combining automated metrics with spot-checking, teams can iteratively improve RAG systems while maintaining focus on topic adherence and factual consistency.