When retrieval-augmented systems (like those using RAG) pull contradictory documents, the generated answer may become inconsistent or misleading. For example, if one document states "Solar panels lose efficiency in cold weather" and another claims "Cold improves their performance," the system might average these claims without resolving the conflict. This can result in an answer like "Solar panels perform differently in cold climates," which fails to clarify the actual relationship. Developers might see this as the model hedging or mixing truths with falsehoods, reducing trust in the output. To mitigate this, systems need better logic to detect and prioritize reliable sources or flag contradictions explicitly.
If no relevant documents are retrieved, the system falls back on its pretrained knowledge, which can be outdated or incorrect. For instance, if asked about a software framework’s 2024 API changes but retrieves no current docs, the answer might describe deprecated features. This manifests as hallucinations (e.g., inventing non-existent endpoints) or generic statements like "Refer to the latest documentation." Developers relying on such answers could waste time debugging incorrect guidance. Solutions include improving retrieval precision with better query expansion or hybrid search (combining keyword and semantic matching) to reduce "zero relevant docs" scenarios.
Outdated or inaccurate documents introduce factual errors even when retrieved. Suppose a medical chatbot retrieves a 2010 study claiming "Vitamin C cures COVID-19." The model might propagate this myth, ignoring newer research debunking it. The final answer could include unsafe advice, misleading users. Developers might not catch this unless the system cross-checks sources against verified databases. To address this, systems should prioritize recency and credibility scores during retrieval and surface source dates to users, allowing them to assess reliability.