What Are Citations in AI Answers?
Links from an AI answer back to the specific source documents used to generate it, allowing users to verify every claim.
A citation is the difference between an answer you can trust and an answer you have to take on faith. Because RAG systems know exactly which retrieved passages informed a response, they can attach the source URL, title, and excerpt for each — something a bare LLM cannot honestly do.
Citations serve three audiences: users, who can verify claims with one click; teams, who can audit what the AI told a customer and why; and content owners, who see which documents actually carry the load.
Well-implemented citations are structured data, not just markdown links — so every channel (widgets, bots, APIs) can render them natively.
How Kelu uses this
Every Kelu answer includes a structured citations array with URL, title, and excerpt per source, rendered across the widget, Slack/Teams bots, the Ask portal, the public API, and MCP responses. Conversation history and traces preserve citations for audit.
Related terms
RAG (Retrieval-Augmented Generation)
An AI architecture that retrieves relevant documents from a trusted knowledge source and passes them to a language model, so answers are grounded in real content instead of the model's training data.
Read definitionGrounding
Tying an AI answer to specific source content, so each claim comes from retrieved passages rather than the model's memory and can be checked against them.
Read definitionAbstention
When an AI assistant declines to answer because its sources don't cover the question, and says so plainly instead of writing a confident guess.
Read definitionHallucination
When a language model generates fluent, confident content that is factually wrong or entirely invented.
Read definitionEval (LLM Evaluation)
A systematic method for measuring the quality of AI-generated answers — often using a second model as a judge — so quality is tracked rather than assumed.
Read definition
From definition to deployment
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