Citation-Grounded Output
Every factual claim in the agent's output is tied to a retrieved source, and ungrounded claims are flagged or removed.
This page is the complete instruction page for one pattern called "Citation-Grounded Output." It explains the problem this pattern solves, the idea behind the solution, when you should (and should not) use it, and what happens afterward — both the good effects and the costs.
This matters because building AI agents is not just about making them clever. It is also about making them safe and predictable. Following a well-tested pattern like this one helps avoid common mistakes, and shows you exactly which safety rules and regulations it connects to, listed under "Standards Mesh" on this page.
Context
RAG that merely shows sources alongside free generation still allows the model to assert things not in the sources. Users see citations and assume grounding that is not real.
Problem
Sources displayed ≠ claims grounded.
Forces
- Fluency vs strict grounding
- Answer completeness vs verifiability
Solution
Constrain generation so each sentence is tagged with the source span that supports it. Post-generation, validate each citation actually supports its claim (lexical overlap plus model-as-judge). Remove or flag unsupported sentences before returning.
Applicability
- Regulated-content agents
- Medical, legal, compliance agents
- Research agents where outputs face review
Anti-Patterns
- Cited sources stapled to ungrounded prose
- "Based on the documents" with no per-claim anchor
Consequences
- +Genuine traceability
- +Lower false-claim rate
- −Stricter outputs, sometimes less fluent
- −Validation pipeline cost