In the context of Retrieval-Augmented Generation (RAG), a hallucination occurs when the model generates information that is not grounded in the data it retrieved or the knowledge it was trained on. Unlike traditional errors, which might stem from incorrect data retrieval or flawed reasoning, hallucinations involve the model inventing plausible-sounding details, facts, or conclusions that lack any basis in reality or the provided sources. For example, a RAG system answering a question about a historical event might fabricate dates, names, or events that don’t appear in the retrieved documents or its training data. These inaccuracies are not mere oversights but reflect the model’s tendency to fill gaps in knowledge with speculative content, often due to overconfidence in its generative capabilities.
Hallucinations differ from simple errors or omissions because they involve active fabrication rather than passive mistakes. An error might occur if the model misinterprets a retrieved document (e.g., confusing two similar terms) or if the retrieval system fetches irrelevant data. An omission happens when the model fails to address part of a query due to incomplete retrieval or insufficient context. In contrast, a hallucination introduces entirely new, unsupported claims. For instance, if a user asks about the health benefits of a food not covered in the retrieved documents, an error might involve misstating a related study, while a hallucination could invent a fictitious study altogether. The key distinction lies in the origin of the mistake: hallucinations are creative but ungrounded outputs, whereas errors stem from flawed processing of valid inputs.
To mitigate hallucinations, RAG systems often employ techniques like source attribution checks, where generated answers are cross-referenced with retrieved documents, or confidence scoring to flag low-certainty outputs. For example, a developer might configure the system to reject answers that lack direct support from retrieved sources or to explicitly state uncertainty when data is missing. These strategies help differentiate hallucinations from errors or omissions, which may require improving retrieval accuracy or refining the model’s training data. Understanding this distinction is critical for developers debugging RAG systems, as it informs whether the fix involves constraining the generator, enhancing retrieval, or adjusting how gaps in knowledge are handled.