{
  "id": 17570492,
  "title": "How Unanswerable Questions Can Inflate RAG Recall@K",
  "url": "https://urgent.news/2026/11/01/how-unanswerable-questions-can-inflate-rag-recall-k",
  "topic": "world",
  "section": "World",
  "published": "2026-11-01T09:27:51.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/infrixo/how-unanswerable-questions-can-inflate-rag-recallk-25h8"
  },
  "original_language": "en",
  "account": "When assessing Retrieval-Augmented Generation (RAG) systems, Recall@K is a key metric that measures whether the retrieval component successfully provides relevant documents. However, an issue arises when a question has no matching documents in the corpus. Suppose you evaluate a RAG pipeline on a dataset of eight questions, with seven answerable and one unanswerable. A naive implementation of Recall@K might assign a perfect score of 1.0 to the unanswerable question, even though no relevant documents were retrieved. This approach misleadingly inflates the evaluation score. The problem stems from the condition `if not expected_ids: return 1.0`, which erroneously awards full credit for retrieval when there are no relevant documents to retrieve. This can significantly impact the overall mean Recall@K score, even though the retrieved documents and retrieval logic remain unchanged. A better approach is to return `None` for unanswerable questions, indicating that retrieval recall is not applicable. When aggregating the scores, these `None` values should be excluded from the mean calculation, and the number of eligible questions should be reported separately. This adjustment acknowledges that an empty expected-document set can represent different scenarios—intentional unanswerability versus incomplete relevance annotations. While excluding unanswerable questions from the retrieval recall metric doesn't mean ignoring them entirely, it does prevent a faulty evaluation from masking actual retrieval performance. RAG evaluation is often used to assess various components like chunking strategies, embedding models, and rerankers. However, evaluation should distinguish between retrieval correctness and abstention behavior (whether the system correctly identifies insufficient evidence). Reporting both the mean retrieval score and the number of eligible questions, while evaluating unanswerable cases separately, provides a more accurate assessment of the RAG system's capabilities.",
  "summary": "Retrieval evaluation is one of the first things we measure when building a Retrieval-Augmented Generation (RAG) pipeline. Metrics like Recall@K help answer a straightforward question: Did the retriever return the documents needed to answer the question? But there's an edge case that can make your evaluation results misleading: questions for which no relevant document exists. The problem Suppose…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}