Skip to main content
Bureau of AI
Category: Validation & Testing

Groundedness Evaluation

Also known as: Groundedness, Groundedness Detection, Faithfulness (related usage)
Simply put

Groundedness evaluation is a way of checking whether an AI system's generated answer is actually supported by the source material it was given, rather than being made up. It is commonly used with retrieval-augmented generation (RAG) systems, where a model retrieves documents and then produces a response based on them. A response is considered 'grounded' when its statements can be traced back to and are supported by the retrieved or input source.

Formal definition

In the evidence provided, groundedness evaluation is described as a metric for assessing the degree to which an output generated by a large language model (LLM)—typically within a retrieval-augmented generation (RAG) pipeline—is supported by the retrieved context or input source material. As commonly implemented, an evaluator decomposes the generated response into individual claims and verifies each against the provided source, flagging content that deviates from or is not supported by that source. Groundedness is frequently cited as one component of broader RAG evaluation frameworks (for example, the 'RAG triad' referenced in the evidence, which pairs groundedness with relevance and factuality). Note that terminology is not standardized across the sources: one source treats 'groundedness' as synonymous with the answer being 'supported by the retrieved documents,' a notion also referred to elsewhere as faithfulness. Groundedness as defined here concerns alignment with a given source and should not be conflated with general factual correctness against the real world, since a response can be grounded in a source that is itself inaccurate. The specific decomposition methods, scoring scales, and thresholds vary by tool and vendor and are out of scope for this definition.

Why it matters

Groundedness evaluation matters because generative AI systems can produce fluent, confident-sounding text that is not actually supported by the source material provided to them. In retrieval-augmented generation (RAG) settings, where a model is expected to answer based on retrieved documents, an ungrounded response introduces content that cannot be traced to the input—a common failure mode often described as fabrication or 'hallucination.' For organizations deploying these systems in customer-facing or decision-support contexts, the ability to measure whether outputs stay tethered to their sources is a practical control for detecting this behavior.

A key limitation that experts insist on preserving is that groundedness measures alignment with a given source, not truth about the world. As the evidence indicates, groundedness concerns whether statements can be traced back to and are supported by the retrieved or input source. A response can therefore be well grounded in a source document that is itself inaccurate, incomplete, or outdated. Groundedness evaluation should not be treated as a substitute for factual verification against reality; in many evaluation frameworks it is paired with separate measures—for example, the 'RAG triad' referenced in the evidence combines groundedness with relevance and factuality. Conflating groundedness with general factual correctness is a frequent and consequential error.

Because groundedness evaluation is an emerging practice with non-standardized terminology, its results should be interpreted with care. The evidence shows that some sources use 'groundedness' interchangeably with 'faithfulness,' and the specific decomposition methods, scoring scales, and thresholds vary by tool and vendor. Treating a single groundedness score as an authoritative or universal measure of output reliability would overstate what the metric provides. It is best understood as one signal, useful for reducing but not eliminating the risk of unsupported outputs, and its meaning depends on the particular implementation and the quality of the sources being checked against.

Who it's relevant to

Data scientists and ML engineers building RAG systems
Those developing or tuning retrieval-augmented generation pipelines use groundedness evaluation to detect when outputs drift from the retrieved context. It helps diagnose whether errors stem from the generation step producing unsupported claims, as distinct from other failures such as poor retrieval relevance—a separation the RAG triad framing in the evidence supports.
Model risk and validation teams
For teams responsible for evaluating and monitoring deployed models, groundedness offers one measurable signal for the risk that a system produces unsupported content. It is important to recognize its scope: groundedness measures alignment with a provided source, not factual correctness against the real world, so it addresses only part of the broader question of model output reliability.
Compliance and oversight functions
Professionals overseeing AI systems in regulated or high-stakes settings may reference groundedness as a control that reduces the likelihood of fabricated or unsupported outputs. Given that terminology is not standardized and that groundedness in a source does not guarantee real-world accuracy, oversight functions should treat it as one component of a wider evaluation and monitoring regime rather than a complete assurance mechanism.
Product and content quality owners
Those accountable for the quality of AI-generated content in customer-facing applications can use groundedness evaluation to identify responses that stray from their intended sources. Because scoring methods and thresholds vary by tool and vendor, product owners should confirm how a given implementation defines groundedness before setting acceptance criteria.

Inside Groundedness Evaluation

Source-Grounding Assessment
The evaluation of whether a model's generated output is supported by, and traceable to, a specified source or retrieved context, rather than fabricated or drawn from parametric memory. Groundedness is typically assessed relative to a defined reference corpus or retrieved evidence.
Claim Attribution
The decomposition of an output into individual factual claims and the mapping of each claim back to supporting spans in the source material. Ungrounded claims are those with no supporting evidence in the provided context.
Evaluation Reference Set
The body of source documents, retrieved passages, or ground-truth context against which groundedness is measured. The scope and quality of this reference set materially affect the result, so its definition is part of the evaluation itself.
Scoring Method
The technique used to produce a groundedness judgment, which may include human review, automated entailment or natural-language-inference checks, or model-based (LLM-as-judge) scoring. Each method has differing reliability and its own limitations.
Failure Categorization
The classification of ungrounded outputs, for example fabricated facts, unsupported inferences beyond the source, or misattributed content. This supports root-cause analysis and targeted remediation.

Common questions

Answers to the questions practitioners most commonly ask about Groundedness Evaluation.

Does a high groundedness score mean the model's output is factually correct?
No. Groundedness typically assesses whether a response is supported by or attributable to a provided source context, not whether the underlying claim is true in the real world. A response can be well-grounded in a source that is itself inaccurate, outdated, or biased. Conversely, a correct statement drawn from the model's parametric knowledge rather than the supplied context may score as poorly grounded. Treat groundedness as a measure of source-faithfulness, and evaluate factual accuracy separately.
Is groundedness evaluation the same thing as hallucination detection?
The two are related but not interchangeable. Groundedness evaluation, as commonly defined, measures the degree to which an output is supported by a given context, and low groundedness is often used as one signal of hallucination. However, hallucination is a broader concept that can include fabricated content unrelated to any provided source, and some hallucination-detection approaches do not rely on a reference context at all. Using a groundedness score as a complete proxy for hallucination risk can leave gaps, particularly for open-ended generation without a defined source.
How do teams typically decide what counts as the 'source context' against which groundedness is measured?
The reference context is usually defined by the system design. In retrieval-augmented setups it is commonly the retrieved passages passed to the model; in summarization it may be the input document. Defining this scope explicitly matters, because a claim may be grounded in one part of the retrieved set but not in the specific passage a reviewer checks. Teams often document what is treated as in-scope context and whether the model's own prior knowledge is permitted, since that choice directly shapes how scores are interpreted.
What are common methods used to score groundedness in practice?
Approaches vary and each has trade-offs. Human review can assess whether each claim traces to the source but is costly and can be inconsistent without clear rubrics. Automated methods include natural language inference or entailment-style checks between claims and context, overlap or similarity heuristics, and LLM-as-judge techniques where a model evaluates support. Automated scorers can themselves be unreliable and may introduce their own errors, so many teams calibrate them against a human-labeled sample rather than relying on them unvalidated.
How does groundedness evaluation fit into ongoing monitoring rather than one-time testing?
Groundedness can be assessed at initial evaluation and re-assessed over time, because performance can shift as retrieval sources, prompts, model versions, or usage patterns change. In many monitoring setups it is tracked as one metric among several, sampled on production traffic or run against a fixed benchmark set. It is worth distinguishing degradation in groundedness caused by model changes from changes in the underlying data or retrieval quality, since the remediation differs.
Where should groundedness evaluation sit within lines of defense and documentation?
Practices differ by organization, but groundedness evaluation is often designed and executed by the model development or product team (commonly associated with the first line) and reviewed or challenged by an independent function (commonly associated with the second line). Documenting the evaluation method, the defined source context, thresholds, sample sizes, and known limitations supports both internal challenge and any external review. Note that a passing groundedness evaluation reduces but does not eliminate the risk of unsupported outputs.

Common misconceptions

Groundedness and factual accuracy are the same thing.
As commonly defined, groundedness measures whether an output is supported by the provided source context, not whether it is true in the world. An output can be grounded in a source that is itself incorrect, and a factually correct statement can be ungrounded if it is not supported by the supplied context. The two properties are related but distinct.
A high groundedness score means the model does not hallucinate.
Groundedness evaluation typically reduces the likelihood of certain hallucinations rather than eliminating them. Scores depend on the reference set, the scoring method, and the sample evaluated; residual risk remains, and automated judges can themselves make errors.
Groundedness evaluation is interchangeable with relevance or answer-quality evaluation.
Relevance asks whether retrieved context or an answer addresses the query, while groundedness asks whether the answer is actually supported by that context. An answer can be relevant yet ungrounded, or grounded yet unhelpful, so these dimensions are typically evaluated separately.

Best practices

Explicitly define and document the reference set against which groundedness is measured, since the evaluation result is only meaningful relative to that scope.
Decompose outputs into individual claims and attribute each to supporting source spans, rather than scoring a whole response as a single grounded or ungrounded unit.
Distinguish groundedness from factual accuracy and relevance in your evaluation design, and report them as separate dimensions to avoid conflating distinct properties.
Where automated or model-based scoring is used, validate it against human judgments on a sample and document its known limitations rather than treating its scores as definitive.
Categorize ungrounded outputs by failure type to support root-cause analysis and targeted remediation.
Treat groundedness evaluation as a measure that reduces and monitors certain risks rather than one that eliminates hallucination, and communicate residual risk accordingly.