Many chatbots now can search the web, but still, they can provide outdated or inaccurate information for various reasons. These models rely on training data with a fixed knowledge cutoff date, meaning they only "know" information available up to that point. When real-time search tools aren't triggered (whether due to computational cost constraints, technical failures, or query classification issues) the chatbot falls back on this static knowledge base, which may no longer reflect current facts.
Even when search functionality works as intended, if search results contain conflicting claims, misinformation, or low-quality sources, the chatbot may inadvertently adopt and reproduce these errors in its responses.
Lightweight models, optimized for speed and efficiency rather than comprehensive accuracy, are more prone to hallucinations and factual errors than their full-sized counterparts. These systems can also struggle with complex or nuanced queries that require multimodal reasoning or involve niche information that isn't well-indexed or easily discoverable through standard web searches.