What Is Groundedness in AI? Measuring Whether an Answer Is Actually Supported
A fluent, confident-sounding answer isn't the same as a correct one — groundedness measures whether a response is actually backed by real, verifiable source material.
Backed by sources, not just sounding right
Groundedness measures whether a model's response is actually supported by verifiable source material — retrieved documents, provided context — rather than simply being fluent and plausible-sounding. An ungrounded answer might still read as completely confident and coherent while having no real basis.
Why this matters especially for RAG
The entire premise of RAG is answering from retrieved material rather than from memory alone, so groundedness is a direct measure of whether that system is actually working as intended, or whether the model is quietly falling back on unsupported hallucination instead.
How groundedness gets checked
Groundedness is typically evaluated by checking whether each claim in a response can be traced back to something in the provided source material — done through careful human review, or increasingly via an LLM-as-a-judge approach that automates the comparison at scale.
Frequently Asked Questions
What is groundedness in AI?
A measure of whether a model's response is actually supported by verifiable source material, rather than just sounding fluent and confident regardless of whether it's actually backed by anything real.
Why is groundedness especially important for RAG systems?
RAG is specifically meant to answer from retrieved material rather than from memory alone, so groundedness directly measures whether that's actually happening, or whether the model is quietly hallucinating instead.