AI Grounding & Citations
AI grounding connects an answer to evidence the system can inspect, while citations identify where particular claims are supported. The skill combines evidence selection, claim attribution and source presentation, so a reader can distinguish a supported statement from an inference or a claim for which the available material is insufficient.
What it is
A grounded response conditions generation on relevant external material rather than relying only on information encoded in model weights. A citation should connect a statement to a source passage, page or other inspectable location. Merely appending a document title or link does not establish support: the cited material must entail the relevant claim, with qualifiers and scope preserved. Grounding and citation are related but separable. An answer may use evidence without exposing it, or display citations that fail to support what the text says. Both behavior and attribution therefore require evaluation.
What the work involves
The practitioner retains source identifiers and passage locations during retrieval, asks for claim-level attribution and validates references against supplied material. Review checks whether a citation exists, whether it supports the statement and whether important statements lack support. Conflicting or outdated sources require an explicit handling policy. Useful artifacts include an attribution schema, annotated claim–passage pairs and an interface that opens the relevant evidence. Generated interpretations should be identified as interpretations when the source does not state the conclusion directly.
Illustrative example
A policy assistant answers whether employees may carry unused leave into a new year. It retrieves the policy section, states the allowed conditions and cites the exact passage. A separate source describes a departmental exception, so the answer explains that exception with its own citation. If the documents do not specify a contractor's eligibility, the assistant says the evidence is insufficient. A link to the general policy homepage would not substitute for support of that narrower question.
Limits and common mistakes
Faithful use of a source does not establish that the source itself is accurate or current. Citations can be fabricated, overbroad or attached to a sentence containing several differently supported claims. Model-based attribution checks can also fail on subtle contradictions. Quality assessment should inspect evidence relationships and source reliability separately. The desired outcome is traceable, appropriately scoped claims, rather than maximizing the number of visible citation markers.
Prerequisites
Grounding is a quality property OF a RAG system — the concept only exists in the context of retrieval-augmented generation
Related skills
- → is part of: Retrieval-Augmented Generation
- → is part of: AI Output Verification
Sources and further reading
- Enabling Large Language Models to Generate Text with Citations
Studies citation quality and the relationship between generated claims and supporting evidence.
Last updated: 2026-10-10