{
  "id": 17674965,
  "title": "Let your LLM say I don't know instead of guessing",
  "url": "https://urgent.news/2026/11/01/let-your-llm-say-i-dont-know-instead-of-guessing",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-01T20:30:07.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/conorbronsdon/let-your-llm-say-i-dont-know-instead-of-guessing-2dog"
  },
  "original_language": "en",
  "account": "Dan Klein, co-founder and CTO of Scaled Cognition, warns that language models generate plausible-sounding yet incorrect answers, a phenomenon known as AI hallucination. This occurs because models predict the most likely next words based on available data, not necessarily factual accuracy. Klein emphasizes that distinguishing truth from guesswork is not something the model does independently. Instead of relying on confidence levels in the user interface as a proxy for correctness, Klein suggests treating every fluent sentence as a claim that requires evidence. He points out that fluency can mask errors, as subtle yet incorrect information often passes unnoticed during quality assurance checks. Enterprise audits reveal that hallucination rates can be five times higher than what customers initially detect, as the model's outputs can be plausible but factually wrong. Chip Huyen, author of AI Engineering, highlights that missing or incorrect context fuels hallucinations. Models are more prone to inventing information when they lack the necessary and correct data to provide accurate answers. To mitigate this, the design should involve providing the model with all relevant facts at the time of answering, allowing for silence when facts are unavailable, and verifying critical information despite the model's inherent limitations. Pre-training exposes models to fluent text without proper labeling of false statements, so the model may fabricate plausible answers even for rare facts. Benchmarking further exacerbates the issue, as \"I don't know\" responses often score as wrong answers, while random guesses may coincide with the correct response in certain scenarios. OpenAI's 2025 discussion on hallucinations suggests penalizing confident errors more heavily than uncertain ones, while Anthropic's work on Claude proposes a default to decline when encountering unrecognized entities, with a \"known entity\" signal that allows for guessing when appropriate. For applications, this means implementing instructions that prohibit uncertainty, as training data tends to reward guessing over abstaining. However, penalizing uncertainty directly conflicts with the incentives that trained the model to guess in the first place. To effectively ground answers while retaining the option to abstain, it is crucial to construct replies using only the context provided during the question, ideally with citations for each claim. This approach reduces the likelihood of inventing details, although it does not guarantee the truth of the sources themselves. Retrieval Augmented Generation (RAG) or search mechanisms can fetch current or private texts and insert them into the context window before generating a response. This method addresses many production issues that might initially appear as model bugs, such as retrieval or document problems, or limitations imposed by the model's knowledge cutoff date. The recommended practice for handling factual queries involves retrieving candidate passages relevant to the question and, if none meet a predefined relevance threshold, returning an abstain response indicating that no trusted source was found. Before generating an answer, instruct the model to use only the provided sources, citing the source text or document ID for each claim. Perform a faithfulness check on the generated answer to ensure it aligns with the retrieved sources, particularly for high-stakes outputs related to money, policy, or compliance. This process includes separate counts for answered, abstained, answered_supported, and answered_unsupported scenarios, enabling more accurate evaluation and reliability assessment. An abstain-and-ground approach can be illustrated with a mock implementation, where retrieval is conducted first, and generation proceeds only if the retrieval is strong enough. If retrieval is weak or empty, the model abstains, informing the user that no trusted source was found. The model's response includes a status indicating abstain, a message explaining the absence of trusted sources, and, if applicable, the draft response or check results. This method ensures that the output explicitly communicates when the system lacks sufficient information to provide an accurate answer.",
  "summary": "Your user asks a factual question. The model returns a polished paragraph. Nothing in the stack threw an error. That is exactly when you are most exposed. A language model generates the most plausible next words, not the most true ones, so a wrong answer can look identical to a right one. That mechanism is what people call AI hallucination : same token prediction, missing or weak facts, fluent…",
  "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."
}