Knowledge cutoff date is disclosed where relevant

Language models have a training cutoff and cannot know about events, legislation, software releases, or market conditions that postdate it. When an application provides no disclosure — neither in the system prompt nor the UI — users treat AI responses as current, leading to decisions based on outdated information. For compliance-sensitive domains (GDPR enforcement updates, securities regulations, medical guidelines), acting on stale AI output can cause real harm. NIST AI RMF GOVERN-1.1 requires transparency about AI system limitations. OWASP LLM09 identifies temporal misinformation as an LLM risk category.

Add a cutoff disclosure to the system prompt with the model's specific training cutoff date:

const systemPrompt = `
Your knowledge cutoff is [MODEL_CUTOFF_DATE]. For questions about recent events,
current prices, active legislation, or software versions, proactively note that
your information may be outdated and direct the user to verify with a current source.
`

Alternatively, add a static footer or tooltip in the chat UI — for example, src/components/chat/ChatPanel.tsx — displaying: "AI responses reflect training data through [date]. Verify time-sensitive information independently."