An LLM answer can contain accurate details, unsupported additions, and misleading combinations in the same paragraph. Checking only whether the overall response sounds plausible hides those differences. A more useful workflow asks which statements require evidence, what the available material supports, and what remains unresolved after a deliberate search.
This guide proposes a claim-level evaluation process for AI-generated answers. It is intended for teams designing review workflows, not as a promise that one model can certify another model’s output. Keep the original answer intact, evaluate the consequential assertions separately, and make the final publishing decision depend on the evidence rather than on a fluent explanation of confidence.
Choose a unit smaller than the whole answer
The FActScore research paper introduces an evaluation that decomposes a generation into atomic facts and measures the percentage supported by a reliable knowledge source. The useful principle is that long-form factuality can be examined at the level of individual assertions rather than reduced immediately to one binary judgment.
For an operational workflow, define the claim unit before measuring performance. A sentence may contain several assertions, while a short phrase may depend on the previous sentence for meaning. Preserve context and qualifiers when splitting an answer. Do not report a historical benchmark result as the accuracy of a current product, and do not assume that performance on one research dataset transfers to your own documents, languages, or risk profile.
Define the evidence boundary before evaluation
Decide whether the task is to check an answer against supplied documents or against a broader collection of external sources. These are different questions. An answer can be faithful to an inaccurate document, and a generally correct statement can remain unsupported by the restricted evidence provided to the evaluator.
Write the boundary into the evaluation instructions and the output. For a document-grounded assistant, label the result as support relative to that document collection. For an open-source research workflow, record which collections were searched and when. Avoid describing a restricted search as comprehensive verification of the entire public record. The boundary is not a technical footnote; it determines what a supported or unsupported label actually means to the reader.
Build a test set from the work you expect
Sample the types of questions, source formats, and answer lengths that the application will encounter. Include straightforward factual requests, missing information, outdated passages, conflicting documents, and ambiguous entities. Preserve examples where the appropriate answer is a refusal to conclude. A test collection made only from easy questions will not reveal whether the system knows when evidence runs out.
Keep the expected claims, relevant passages, and reviewer rationales together. Create a held-out set that is not repeatedly used to tune prompts. Mark the origin and permissions of each document, and avoid putting private material into an evaluation environment without authorization. Treat the collection as a versioned asset. Otherwise, a score may change because the evidence changed rather than because the model became better or worse.
Separate retrieval failure from assessment failure
Suppose a fictional document clearly states that the Atlas catalog launched in 2021, but the retrieval stage supplies only a paragraph about a 2024 redesign. An assessor that returns insufficient evidence may be behaving correctly. Calling this an assessment-model error would lead the team toward the wrong fix.
Evaluate the pipeline in layers. First, ask whether the claim was extracted faithfully. Then check whether relevant evidence was available and retrieved. Next, judge whether the assessment follows from that evidence. Finally, inspect whether the interface presents the result accurately. A strong internal evaluator should be able to distinguish these cases. Otherwise, an apparent improvement in one component can hide a regression elsewhere in the workflow.
Use more than one operational measure
For a proposed internal scorecard, track supported-claim precision, evidence coverage, unresolved claims, and reviewer reversals separately. Define the denominator for every measure. If an answer omits difficult information, it may look precise while being unhelpful. If a system labels everything unresolved, it may avoid confident errors while failing to complete the task.
Report results by relevant slices rather than only as a global average. A system may behave differently on quantities, dates, quotations, and causal statements. Use paired examples when comparing versions so that each version faces the same claims and source material. Include review effort and time to resolution in operational comparisons. The goal is not to produce the most attractive single number; it is to understand which decisions the system can support.
Treat model judgments as reviewable proposals
An LLM evaluator can produce a proposed assessment and a concise evidence rationale. Require it to identify the exact passage that supports or conflicts with the claim, and allow it to state that the evidence is inadequate. Do not ask for a confident answer when the permitted source collection cannot resolve the question.
Use human review for consequential cases and for a sampled portion of apparently straightforward results. Reviewers should inspect the cited material, not merely agree with the evaluator’s explanation. Where feasible, compare judgments without revealing which model produced the answer under review. Record disagreements and their reasons. Repeated disagreements about the same label definition may indicate a flawed rubric rather than an unreliable individual reviewer or a weak model.
Probe meaning, not just matching words
Build adversarial examples that change a small but important detail: a year, a percentage denominator, a geographic scope, an attribution, or a negation. Pair a claim with a passage that shares many keywords but concerns a different entity. Include a statement that combines individually supported facts into an unsupported causal story.
These tests are proposed diagnostic tools, not claims that a particular benchmark covers every failure mode. Their value is in revealing whether the system distinguishes relevance from support. A passage can mention the same topic without proving the assertion. Ask reviewers to mark the exact point where an inference goes beyond the evidence. That annotation is more useful for improvement than a generic note that an answer “hallucinated.”
Plan the release and regression process
Before replacing a component, run the existing and proposed versions on the same evaluation collection. Inspect cases that changed from unresolved to supported especially carefully. A lower abstention rate can represent better retrieval, but it can also represent a willingness to decide without enough evidence. Compare explanations and passage selections, not only final labels.
Set a release rule that reflects the consequences of error in the intended application. Preserve the prior configuration so a problematic update can be reversed. After release, collect corrected assessments and newly encountered failure types for a separate review process. Do not silently rewrite the held-out collection every time an error appears; maintain a stable comparison set and a growing challenge set with clear version histories.
Communicate the result with its limits
A user-facing result should distinguish support, conflict, missing evidence, and processing failure. Show the claim and evidence together. Describe the scope of the check in ordinary language, including whether it examined supplied documents or searched additional sources. A percentage without a denominator, evidence boundary, and calibration explanation is not a useful substitute for that context.
Use the LLM fact-checking topic guide to define the workflow, then pair it with the citation-checker guide to evaluate the references attached to answers. The objective is not to make an AI answer look more authoritative. It is to make unsupported parts easier to find, supported parts easier to inspect, and uncertain parts harder to mistake for established facts.
