RAG groundedness is the degree to which a retrieval-augmented generation system’s answer is supported by the retrieved context it provides as evidence. A grounded answer makes claims that can be traced to specific passages, while an ungrounded answer contains statements that are not justified by the retrieved sources or that go beyond them.
What is RAG Groundedness?
In a RAG pipeline, the LLM is conditioned on retrieved chunks, but it can still generate plausible statements that are not present in those chunks. Groundedness measures whether the output is faithful to the evidence. It is related to, but distinct from, overall correctness. An answer can be grounded yet wrong if the retrieved evidence is wrong or outdated. Conversely, an answer can be correct but ungrounded if it uses the model’s parametric memory without citing or relying on the retrieved passages.
Groundedness is often operationalized as claim-level support. The system extracts atomic claims from the answer, then checks whether each claim is entailed by, or at least supported by, the retrieved text. Some pipelines also verify citation correctness by ensuring that every cited chunk contains the statement it is used to justify.
Improving groundedness typically involves better retrieval, stronger prompts that force evidence-based answers, and verification steps. It also includes abstention behavior: if evidence is missing, the model should say it cannot answer rather than inventing content.
Where it’s used and why it matters
Groundedness is a primary quality target for enterprise RAG assistants, compliance and policy Q&A, and support bots where incorrect statements create risk. It matters because groundedness is a practical proxy for trust. When users can trace claims to sources, they can verify decisions and detect errors. Groundedness is also central to reducing hallucinations, since hallucinations are often ungrounded claims.
Examples
- Grounded: “The SLA is 99.9%” with a citation pointing to the SLA clause in a retrieved policy.
- Ungrounded: “The SLA is 99.99%” when the retrieved context does not mention an SLA at all.
- Partially grounded: A multi-sentence answer where only some claims are supported by the retrieved chunks.
FAQs
- How is groundedness different from correctness?
Groundedness asks whether the answer is supported by provided evidence. Correctness asks whether it is true in the real world. - How do teams measure groundedness?
Using human review, LLM judges with rubrics, and automated claim-checking against retrieved chunks. - Do citations guarantee groundedness?
No. Models can fabricate citations, so citations should be validated. - How can you improve groundedness?
Improve retrieval and reranking, require evidence quotes, add verification passes, and enforce abstention when evidence is missing.
