A RAG system without citations forces users to take answers on faith. Citations enable verifiability — if an answer is wrong, the citation reveals the discrepancy. They build trust, expose hallucinations, and allow users to read the full source context.
Legal research, medical information, and financial analysis require that every claim be attributable to a verifiable source. Citations are not optional for these use cases — they are a safety requirement.
Source labels in context — Format each chunk with a source header: "[Source: Product Manual, Section 3.2]". The model learns to reference sources in its response naturally.
Structured JSON output — Instruct the model to return an object containing the answer and an array of cited source IDs. The application looks up metadata and renders clickable links.
Span-level attribution — Map each sentence in the answer to the specific retrieved chunk it came from. The gold standard for high-stakes applications. Requires careful prompt engineering or post-processing.
If the model makes a claim that cannot be traced to any retrieved chunk, it is either drawing on parametric memory (hallucination risk) or making an inference. Systems can flag uncited claims by checking whether each sentence in the answer is supported by a retrieved source.
Always store original chunk text alongside the vector. Always store source metadata: document name, URL, date, section, verification status.
Render citations as interactive links. Signal source trustworthiness — verified documentation vs. informal notes — through visual indicators.
Ask the AI assistant about citation strategies, structured output for attribution, or detecting hallucinations through source checking.