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Detect production LLM failures with two different control loops: ground factual claims in trusted evidence, and validate every generated result against a deterministic application contract. A fluent answer, agreement between sampled answers, or valid JSON is not proof that the content is true or safe to use. Build versioned test cases from real traffic, measure false positives and false negatives on held-out examples, preserve the evidence behind each verdict, and run the same checks after deployment. Keep enforcement outside the model at tool and transaction boundaries, with human approval for consequential actions. This approach catches unsupported claims, malformed structures, policy violations, and business-rule errors without pretending that one detector score can establish correctness.
What is the difference between a hallucination and an invalid output?
A hallucination is a factual or evidentiary failure; an invalid output violates the format or rules required by the consuming application. They can occur independently, so one check cannot replace the other.
| Failure class | Typical example | Primary test | What the test cannot prove |
|---|---|---|---|
| Unsupported or contradicted claim | An answer invents a product limit or cites a document that does not contain the cited statement. | Compare each material claim with relevant, trusted evidence and record the supporting passage or contradiction. | That the source itself is current, complete, or authoritative beyond your defined trust policy. |
| Malformed structure | Truncated JSON, invalid XML, or prose where a machine-readable object is required. | Parse with a strict parser and reject malformed data. | That a successfully parsed value is factually correct. |
| Schema violation | A required field is absent, a number is emitted as text, or an enum contains an unapproved value. | Validate required fields, types, ranges, and enumerations in application code. | That the values make sense for the user or the real-world situation. |
| Semantic or business-rule violation | A valid order object requests a quantity above a customer limit or approves an action for the wrong account. | Run domain invariants, authorization checks, and state-transition rules outside the model. | That an allowed action is wise; policy and human review may still be required. |
Write your definitions in operational language. “Hallucination” might mean an unsupported answer, a contradiction of a source, a fabricated citation, or a claim not supported by the documents retrieved for that request. “Invalid” might mean a parse error, missing field, wrong type, out-of-range value, disallowed action, or violation of an invariant. Version these definitions with the prompt, model, retrieval settings, and policy so release-to-release measurements remain comparable.
How should I define contracts and failure categories before launch?
Start with the downstream consumer, not the model. Document what the application must receive, which evidence must accompany an answer, and what happens for every possible check result.
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Specify the output contract
- Define the serialization format and parser mode (for example, strict JSON with no trailing prose).
- List required and optional fields, primitive types, array cardinality, allowed enum values, units, and numeric ranges.
- State cross-field invariants, such as an end date that cannot precede a start date or a total that must equal the sum of line items.
- Identify fields that are advisory versus fields that can trigger a transaction, tool call, notification, or access decision.
- Define an explicit “cannot answer” or “needs clarification” representation rather than forcing the model to invent a value.
Define factual support
Decide which claims require citations, which source classes are trusted, how recent a source must be, and whether a claim may rely on several documents. For retrieval-augmented generation, preserve the exact passages supplied to the model; a later search cannot reliably reconstruct what the model saw.
Version the whole decision surface
Store identifiers for the model and provider, prompt or policy revision, retrieval index and ranking configuration, tool definitions, detector version, threshold set, and contract revision. A detector result without these versions is difficult to reproduce and can conceal a regression introduced by a seemingly unrelated change.
How do I build an evaluation set that represents production traffic?
Use realistic requests and known failure modes, then hold out examples that are never used for tuning. The Singapore Responsible AI Playbook recommends representative held-out examples and measurement of precision, recall, false-positive and false-negative rates, over-refusal, latency, cost, completion after interventions, and downstream failures. Its production-integration guidance also advises testing the complete system, with and without the guardrail where practical.
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Collect the right cases
- Normal answerable requests across languages, user roles, document types, and traffic volumes.
- Unanswerable questions, ambiguous wording, false premises, and requests that require clarification.
- Relevant, irrelevant, stale, duplicated, and conflicting retrieved documents.
- Malformed-output cases: truncation, wrong types, missing fields, invalid enum values, and extra prose.
- Tool scenarios involving denied permissions, invalid arguments, stale state, retries, and partial failures.
- Known incidents and near misses, including examples that previously passed a detector.
Label outcomes and evidence
For each case, record the expected user-facing outcome, the trusted reference or policy, whether each material claim is supported, and the acceptable contract values. Have a second reviewer label a sample and adjudicate disagreements. Keep a held-out split for release decisions; tuning on every example makes a detector look better without showing whether it generalizes.
Evaluate the system, not only the guardrail
A detector can perform well alone and still fail when retrieval, prompts, tools, and other guardrails alter the input or output. Compare end-to-end journeys: answer quality, intervention, abandonment, latency, and downstream success with the detector enabled and, in a controlled test, disabled. Never disable a production safeguard merely to obtain a comparison; use a sandbox, replay, or shadow path.
How can I check whether factual claims are grounded?
Use a claim-by-claim evidence trail. Extract material claims, retrieve or select trusted passages, judge whether each passage supports, contradicts, or fails to address the claim, and store the verdict with the claim and source location.
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Preserve an auditable claim record
| Record | Purpose |
|---|---|
| Claim text and normalized subject | Shows exactly what was evaluated and helps group equivalent claims. |
| Source identifier, version, timestamp, and passage offsets | Allows an auditor to reopen the evidence the evaluator used. |
| Verdict: supported, contradicted, or not addressed | Separates lack of evidence from evidence of falsity. |
| Evaluator version and confidence | Exposes changes in the judge or rubric over time. |
| Human-review outcome, when sampled | Estimates evaluator error and supplies new edge cases. |
For agentic workflows, link both claims and actions to the documents and tool results that led to them. NIST describes this evidence goal as moving beyond “the AI said so” to “here is what the AI found, where it found it, and how the evidence supports the conclusions.” Its evaluation-probe project is a pattern for auditability, not a promise that an automated probe perfectly establishes truth.
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- Require a current primary source for regulated or rapidly changing facts.
- Reject a citation when the passage is merely topically related but does not entail the claim.
- Mark conflicting sources for clarification or review instead of averaging them into a confident sentence.
- Distinguish “not addressed” from “contradicted”; the response policy can differ for each.
- Sample human reviews to estimate both missed hallucinations and unnecessary flags.
Fluent prose and repeated agreement are not evidence. A model can repeat the same unsupported statement, and a retrieval system can return a confidently wrong or stale document.
How do I validate generated output deterministically?
Parse and validate in application code before any downstream use. Deterministic checks should reject or route invalid data; a model-based repair step is optional and must itself be measured.
- Capture the raw response. Store the unmodified bytes, provider status, finish reason, and token or size limits before attempting repair.
- Parse strictly. Reject truncated data, duplicate keys if your parser permits them, trailing prose, invalid encodings, and unsupported numeric forms.
- Apply the schema. Check required fields, types, nullability, array limits, enum membership, string formats, and numeric ranges.
- Run semantic invariants. Check relationships among fields, unit conversions, date logic, and domain constraints.
- Authorize actions separately. Confirm tenant, user, session, resource, and current state before a tool call or write operation.
- Choose a measured response. Allow and record, ask for clarification, warn, redact, retry, block, or escalate according to documented risk.
- Record the outcome. Include original failure, retry count, final result, added latency, user-visible message, and downstream effect.
Do not treat syntactic success as factual success. A perfectly valid object can contain an invented account number, an unsupported diagnosis, or an unauthorized instruction.
Which detection methods should I combine?
No single detector covers every failure. Select methods by the property you need to test and measure their end-to-end effect.
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| Approach | Useful for | Limitations | Measure |
|---|---|---|---|
| Deterministic contract validation | Parsing, schema, types, ranges, enums, and business rules. | Cannot establish factual support. | Contract coverage, failure handling, latency, and downstream impact. |
| Evidence-grounding evaluator | Whether answer claims are supported by retrieved or curated sources. | Depends on source quality, claim extraction, and evaluator reliability. | Grounded-claim recall, false alarms, traceability, and domain-specific performance. |
| Model-as-judge or guardrail | Policy, relevance, semantic support, and rubric-based checks. | Adds cost and latency; can miss issues or over-block and is itself fallible. | Precision, recall, over-refusal, cost, latency, and adversarial robustness. |
| Self-consistency sampling | Disagreement among independently sampled answers as an uncertainty signal. | Agreement does not prove truth; sampling increases inference cost. | Detection lift against a fixed baseline and added cost per useful catch. |
| Human review | Ambiguous, novel, or high-impact cases needing context. | Does not scale to every request and reviewers may disagree. | Reviewer agreement, queue time, escalation rate, and sampled error estimates. |
The NeMo Guardrails evaluation documentation describes a SelfCheckGPT-style rail that samples answers and checks consistency. Its documented tests used manually curated false-premise questions and older model runs in 2023 and March 2024; reproduce an evaluation on your own workload rather than generalizing those historical results.
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How should thresholds map to production responses?
Keep detection separate from response. The Singapore playbook uses that exact principle in its production integration guidance: first define what a detector observes and how it scores, then decide what each outcome does.
Document every control point
- Where it runs: input, retrieved context, generated answer, structured object, or tool action.
- What it inspects and which evidence or policy version it uses.
- Possible outcomes, including timeout, evaluator error, and missing evidence.
- Owner, on-call route, and maximum acceptable latency.
- Fallback behavior when the detector or provider is unavailable.
Choose actions by harm and reversibility
Low-risk unsupported wording may trigger a citation request or clarification. A contract error may allow one bounded retry. A payment, account change, deletion, safety decision, or external communication should be blocked or sent to an authorized human when evidence, permission, or state is uncertain. Tune thresholds against the cost of false positives and false negatives, not against a visually pleasing score.
When retries are allowed, cap their count and preserve the original failure. A rising retry rate can be the first sign of a prompt, model, or provider regression; silently returning the final attempt hides that signal.
What should I monitor after deployment?
Establish a baseline during a controlled rollout, then alert on actionable changes rather than ordinary noise. The Singapore monitoring guidance recommends tracking guardrail actions, user effects, sampled errors, operational health, and distribution shifts.
| Monitoring area | Signals to retain |
|---|---|
| Detection behavior | Trigger rates by category and journey; warnings, redactions, retries, blocks, and escalations. |
| Quality | Sampled false positives and false negatives, human overrides, user reports, and downstream task success. |
| Reliability | Latency, timeouts, evaluator and provider errors, queue depth, and tool-call denials. |
| Distribution | Changes in input topics, detector scores, retrieved-document patterns, output length, and trace length. |
| Change context | Model, prompt, retrieval, policy, tool, schema, and threshold versions for every event. |
Prepare incident routes before launch: a scoped way to restrict a guardrail, tool, or journey; a safer fallback; human escalation; and procedures to preserve traces and configuration versions. Protect logs because prompts, documents, outputs, and tool results can contain personal or confidential data. The Singapore monitoring and incident-response guidance covers these operational preparations.
The UK government’s voluntary AI cybersecurity code, published 31 January 2025, calls for logging system and user actions and analyzing logs for anomalies, unexpected behavior, and changes in model performance. Read the Code of Practice for the full security context.
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How do prompt injection and tool use change the controls?
Prompt injection can alter generated text, retrieved-context handling, or tool use, so hallucination checks are not authorization controls. OWASP recommends defense in depth: screen inputs and outputs where useful, but validate permissions and parameters in application code and treat model output as untrusted at its destination.
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- Validate every tool name and argument against an allowlist, session context, tenant, resource, and current state.
- Use least-privilege credentials and separate read from write capabilities.
- Require confirmation or human approval for irreversible or high-impact actions.
- Log the proposed action, the authorization decision, and the resulting tool response.
- Test malicious instructions in documents, web pages, tool results, and user messages, not only in direct prompts.
See OWASP’s prompt-injection prevention guidance for layered mitigations. A model-based guardrail can itself be manipulated and should never be the only control protecting a consequential operation.
OpenAI’s misalignment-monitoring documentation likewise warns that monitoring can miss issues or flag legitimate activity, and that application safeguards, including human approval, remain important for consequential actions. Coverage depends on API mode and preserved conversation context, so verify the current coverage details when using that feature.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I measure whether the detector is getting better?
Report metrics with their definitions, dataset, model and version, organization, and evaluation year. The reviewed sources do not establish a current, cross-system hallucination rate, so a single percentage should not be presented as universal.
- Precision: the share of flagged cases that truly meet your failure definition.
- Recall: the share of true failures that the detector catches.
- False-positive and false-negative rates: show the two error directions separately.
- Over-refusal: valid requests blocked, redirected, or needlessly qualified.
- Intervention completion: whether users finish after a warning, redaction, retry, or redirect.
- Downstream failure reduction: incidents, reversals, support contacts, or rejected transactions after enforcement.
- Operational cost: added tokens, inference cost, latency, timeouts, and review workload.
Break results down by domain, language, user journey, retrieval quality, and risk tier. A detector with high aggregate recall may miss the rare category that matters most, while a very strict rule can reduce apparent errors by refusing useful work.
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Introduce controls in stages so you can learn their error profile without silently changing user outcomes.
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- Offline evaluation: run the versioned held-out set, inspect disagreements, and set provisional thresholds.
- Shadow mode: execute detectors without changing responses; compare predicted actions with downstream outcomes and sample reviews.
- Limited enforcement: apply controls to a low-risk journey or small traffic fraction with an on-call owner.
- Risk-tier expansion: enforce deterministic contracts broadly, while routing high-impact factual or action decisions to review.
- Change gates: rerun the held-out and incident sets after model, prompt, retrieval, provider, tool, schema, or policy changes.
- Retirement and refresh: remove stale examples, add newly observed misses, and preserve prior versions for comparison.
Before general availability, verify that every block, retry, warning, and escalation has a user-facing message; that operators can locate the evidence and configuration for an event; and that disabling one component does not bypass authorization at the destination.
What are the main limitations of automated hallucination detection?
Evidence quality, evaluator reliability, and distribution shift limit every automated approach. A detector can be uncertain because the source is incomplete, the claim was extracted incorrectly, or the task has no objectively verifiable answer. Treat its score as a decision signal, expose uncertainty to the appropriate reviewer or user, and continue sampling human judgments.
Coverage also changes when providers alter models, context windows, safety behavior, or tool interfaces. The UK code’s logging principle and the Singapore playbook’s versioned monitoring practices are therefore operational requirements, not one-time launch tasks.
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Frequently Asked Questions
Can repeated sampling prove that an LLM answer is true?
No. Repeated samples measure agreement under similar model behavior, not correspondence with reality; a shared misconception can appear consistently. Use sampling to prioritize uncertainty checks, then verify material claims against trusted evidence or a qualified reviewer.
What should I do when no trusted source can answer the question?
Return an explicit unknown or clarification state that your contract supports, rather than filling the gap with a plausible sentence. Record the request as an unanswerable case so product owners can decide whether to add data, change scope, or route it to a person.
How much prompt and output data should production logs retain?
Retain enough content, source passages, tool results, and version identifiers to reproduce a decision, but apply data minimization, access controls, encryption, and a documented retention period. Redact or tokenize sensitive fields before sending traces to analytics systems, and keep a separate restricted path for incident investigation.
When should a human approve an LLM action?
Use approval when an error could cause significant harm, is difficult to reverse, affects a person’s rights or money, or cannot be verified automatically. The reviewer should see the proposed action, relevant evidence, authorization context, and any detector warnings—not just the model’s prose.
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