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Why a fluent answer is not proof
A generated response does not verify itself. Fluency, confidence, and machine-readable structure are properties of how an answer is presented; none establishes that its claims are true or complete.
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Reliability depends on the whole application workflow: the model and prompt, the data supplied or retrieved, any tools the model can use, how the output is handled, and the checks performed before a user or another system acts on it. There is no universal accuracy threshold that makes every application safe to trust.
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A schema can constrain the response’s shape, such as requiring particular fields or types. OpenAI’s Structured Outputs documentation describes this kind of format control. It does not independently establish that a value is factual, that a claim is supported, or that an important qualification has not been omitted.
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So treat parsing as a syntax check, not a truth check. An answer can pass schema validation and still be false, unsupported, or misleading. Validate business rules, allowed values, ranges, and required information separately in trusted application code.
How to check factual claims against evidence
For factual answers, identify an appropriate source of truth for the task: for example, a trusted database or API, a curated reference corpus, or a human-reviewed source. Compare the claims that matter with that evidence rather than relying on the model’s own assurance.
NIST’s Building Evaluation Probes into Agentic AI project describes comparing agent claims with a human-curated reference corpus and creating machine-readable audit trails. It frames citation quality around three questions:
- Faithfulness: Does the source actually support the claim?
- Completeness: Does the answer preserve the source’s full message, including relevant qualifications?
- Sufficiency: Is the source strong enough to support the claim being made?
A citation alone is not a check: it may point to material that does not support the attached statement. Preserve enough information for a reviewer to inspect the claim, its source, the check result, and the rationale. NIST describes this work as an ongoing research project, not a universal production verifier. Its project page puts the aim this way: “The goal is to move beyond ‘the AI said so’ to better understand ‘here is what the AI found, where it found it, and how the evidence supports the conclusions.’”
How to test the application you actually ship
Test the workflow, not a polished demonstration. Build representative inputs and define what a good result means for the task and its users. Review failures, then rerun evaluations after meaningful changes to the model, prompt, retrieval data, tools, or output handling.
OpenAI’s evals guide describes ways to define evaluations and graders. Use criteria that reflect the real task, including cases where the right result is to abstain, request clarification, or escalate for human review. A strong result on a test set is evidence about those tested cases; it does not establish universal correctness or guarantee future behavior. Pair evaluation with evidence-grounding checks where factual support matters, as in the approach described by NIST.
Keep model output inside application-controlled boundaries
Do not let generated text make authorization decisions or bypass application rules. Enforce permissions and allowed operations in trusted code, and validate types, ranges, identities, and actions before passing output to another component or carrying out a consequential operation.
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OWASP’s 2025 Top 10 for LLM Applications discusses risks including misinformation from hallucination or confabulation and problems with insufficient validation or sanitization of model output. Its earlier v1.1 guidance, published in 2023, is another edition of that security guidance. Treat retrieved content and tool output as data to evaluate, not as privileged instructions. Security recommendations evolve, so distinguish guidance by edition rather than treating a past list as a permanent standard.
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Choose verification to fit the risk
The right checks depend on what the answer is for; there is no single verification method or accuracy figure that applies to every application. Choose controls by considering:
- Claim type: Is the answer a stable fact, current information, a calculation, subjective generation, or high-impact advice?
- Evidence source: Is there no external check, a trusted database or API, a curated corpus, or a source that needs human review?
- Failure consequence: Would an error cause inconvenience, financial or operational loss, privacy or security exposure, or harm to people?
- Verification method: Would deterministic validation, retrieval and source matching, an independent evaluator, human approval, or layered checks address the risk?
- Traceability and operations: Can a reviewer see the input, model or output version, supporting material, validation result, and action taken—and is the evidence depth and review burden practical?
For low-consequence tasks, lightweight checks may be enough. As the possible harm rises, add stronger evidence requirements, independent checks, and human approval where appropriate. The reviewed guidance does not prescribe a universal threshold: the application team must set one for the particular task and its consequences.
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