Dataconsultant tests generative AI applications for factual errors, unsupported claims, invented sources, citation defects, and weak uncertainty handling. We help AI, product, risk, compliance, and technology teams build evidence about system reliability, prioritise material failures, improve grounding and controls, and establish repeatable evaluation before release and during operation.
Illustrative evaluation structure only; scores and thresholds are defined for each client use case.
A hallucination testing service evaluates whether a generative AI system makes claims that are false, unsupported by available evidence, inconsistent with cited sources, fabricated, or expressed with unjustified confidence. The work combines risk analysis, scenario design, reference evidence, repeatable execution, human review, automated checks, and remediation planning.
Effective testing does not rely on one generic “accuracy” score. It separates claim correctness, groundedness, citation validity, completeness, relevance, uncertainty handling, retrieval quality, and business materiality so decision-makers can understand where failures occur and which controls are appropriate.
AI outputs may appear fluent even when evidence is missing or incorrect. Testing helps organisations identify where that behaviour can cause customer, operational, regulatory, financial, or reputational harm.
Risk: A chatbot, copilot, or assistant presents invented facts, policies, figures, or explanations as reliable.
Response: Build scenario coverage, claim-level review, severity classification, and evidence-backed acceptance criteria.
Risk: References exist but do not support the claim, point to the wrong passage, or are fabricated.
Response: Validate citation existence, source quality, claim entailment, attribution completeness, and link integrity.
Risk: A retrieval-augmented system ignores relevant context, mixes sources, or generates beyond supplied evidence.
Response: Separate retrieval performance from generation performance and test both under realistic conditions.
Risk: Generic benchmarks miss the organisation’s terminology, policies, data, languages, user behaviour, and material decisions.
Response: Create domain-specific tests mapped to users, workflows, obligations, and credible failure modes.
Risk: Provider updates, prompt changes, source changes, or configuration changes introduce regressions.
Response: Establish versioned evaluation sets, release gates, trend reporting, and retesting triggers.
Risk: Leaders cannot explain what was tested, how failures were judged, or what limitations remain.
Response: Maintain traceable scenarios, criteria, findings, reviewer decisions, assumptions, and residual-risk records.
Scope is tailored to the system, evidence sources, user journeys, decision impact, languages, model architecture, and required assurance depth.
Define relevant failure types, user harms, materiality levels, test boundaries, evidence standards, review roles, pass criteria, and reporting needs. The taxonomy may distinguish intrinsic and extrinsic factual errors, unsupported claims, fabricated entities, citation defects, temporal errors, contradiction, omission, and misleading certainty.
Create realistic user questions, edge cases, ambiguity tests, multi-turn scenarios, source-conflict cases, missing-evidence conditions, long-context tests, prompt-injection interactions, and domain-specific challenge sets. Coverage is prioritised by likelihood, impact, user group, workflow, and regulatory sensitivity.
Identify authoritative sources, prepare reference answers or claim sets, document acceptable variants, resolve source conflicts, define time validity, and record where expert judgement is required. This creates a defensible basis for evaluating outputs rather than treating one model’s answer as truth.
Assess claim correctness, support from provided context, attribution quality, citation existence, source-to-claim entailment, completeness, consistency, and unsupported extrapolation. Results can be analysed by use case, topic, language, user group, model, prompt, source, severity, and failure class.
Separate failures caused by missing retrieval, weak ranking, poor chunking, stale content, access filtering, context truncation, prompt design, model behaviour, tool errors, or answer synthesis. This helps teams direct remediation to the responsible layer rather than only changing the model.
Evaluate whether the system signals uncertainty, asks clarifying questions, refuses unsupported requests, distinguishes known from unknown information, and escalates high-risk cases. Testing can cover confidence language, fallback messages, human review, source disclosure, and safe completion rules.
Translate findings into changes to source content, retrieval, prompts, system instructions, answer policies, citation controls, guardrails, workflow design, monitoring, and user experience. A versioned regression suite can be created for future model, platform, content, or policy releases.
Final outputs are agreed during scoping and may vary according to assurance purpose, system maturity, and regulatory context.
| Deliverable | What it contains | Decision supported | Client participation |
|---|---|---|---|
| Evaluation strategy | Scope, system boundaries, risk taxonomy, test dimensions, evidence rules, roles, thresholds, and limitations | What must be tested and why | Approve use cases, risks, and acceptance criteria |
| Scenario and prompt library | Normal, edge, adversarial, ambiguous, multi-turn, and missing-evidence tests | Whether coverage reflects real use | Provide journeys, incidents, and priority topics |
| Reference evidence pack | Authoritative sources, expected claims, acceptable variants, conflicts, dates, and expert notes | How correctness will be judged | Validate authoritative evidence and domain interpretation |
| Evaluation dataset and rubric | Inputs, outputs, labels, severity, criteria, reviewer guidance, and traceability fields | Whether testing is repeatable | Review materiality and specialist decisions |
| Findings and failure analysis | Rates, examples, patterns, root-cause hypotheses, affected journeys, and residual uncertainty | Whether release or remediation is appropriate | Confirm operational impact and risk ownership |
| Remediation backlog | Prioritised changes across content, retrieval, prompts, controls, UX, monitoring, and governance | What should change first | Assign owners, feasibility, and delivery priorities |
| Regression and monitoring plan | Versioned tests, release gates, trigger events, sampling, alerts, review cadence, and reporting | How assurance continues after launch | Integrate with product, model, data, and incident processes |
The process is adapted to the use case and works for pre-release assurance, targeted investigation, vendor comparison, remediation, or ongoing regression testing.
Confirm business purpose, users, decisions, system boundaries, obligations, risk appetite, and assurance questions.
Primary output: Approved scope and risk-based evaluation plan.
Understand models, prompts, tools, retrieval, source content, access controls, languages, logging, and known incidents.
Primary output: System map, evidence inventory, assumptions, and test dependencies.
Create scenarios, prompts, reference evidence, rubrics, severity rules, reviewer guidance, and sampling logic.
Primary output: Traceable evaluation dataset and execution protocol.
Run repeatable tests across agreed configurations and combine automated checks with human domain assessment.
Primary output: Labelled results, failure examples, and quality-control records.
Analyse failure patterns, likely causes, affected journeys, severity, control gaps, and remediation options.
Primary output: Findings report and prioritised remediation backlog.
Validate material fixes, establish regression coverage, define release gates, and transfer methods to internal teams.
Primary output: Retest evidence, regression suite, and monitoring plan.
The service is vendor-neutral and can work with suitable client-controlled environments, platform evaluation capabilities, and approved testing tools.
Hallucination testing should operate within the organisation’s wider AI governance, data protection, information security, model-risk, supplier-risk, and change-management arrangements.
Metrics should be interpreted by scenario mix, severity, evidence quality, and user impact. A single aggregate rate can hide material failures.
Share of evaluated claims judged correct against authoritative evidence, with separate reporting for material and non-material errors.
Share of claims that are entailed by the permitted context or source collection rather than generated beyond available evidence.
Whether references exist, resolve correctly, support the associated claim, and cover all claims for which attribution is required.
Whether confidence language, clarification, refusal, fallback, and escalation behaviour match the strength of available evidence.
Whether required evidence is retrieved, ranked, accessible, current, and included within the model’s usable context.
High-severity factuality failures detected after release relative to monitored interactions, with incident and change context.
| Model | Suitable when | Typical scope | Commercial considerations |
|---|---|---|---|
| Focused assessment | A specific application, release, incident, or risk question needs independent review | Defined scenarios, targeted execution, findings, and recommendations | Fixed or milestone-based scope after discovery |
| Pre-release assurance | A pilot or production launch needs documented evidence and release criteria | Risk strategy, representative suite, execution, review, remediation, and retest | Scope depends on use cases, configurations, languages, and evidence preparation |
| Evaluation framework build | Internal teams need reusable methods, datasets, rubrics, tooling, and governance | Framework design, implementation support, documentation, training, and handover | Phased delivery with client product, AI, data, risk, and engineering participation |
| Managed regression testing | Models, prompts, content, or platform components change regularly | Scheduled execution, trend reporting, change review, issue escalation, and suite maintenance | Recurring service based on volume, frequency, environments, and review depth |
| Specialist capacity | A programme needs evaluation analysts, data specialists, domain reviewers, or assurance support | Dedicated roles working within client governance and delivery processes | Time-based or capacity-based model with defined responsibilities |
A reliable estimate requires initial scoping. Fixed claims about cost or duration are not appropriate without understanding the system, use cases, evidence, and assurance expectations.
Hallucination testing evaluates whether an AI system produces factual errors, unsupported statements, invented entities or sources, misleading citations, contradictions, or unjustified certainty. A robust approach uses risk-based scenarios, authoritative evidence, documented scoring rules, repeatable execution, and human review for material or ambiguous cases.
The service can be adapted to chatbots, copilots, retrieval-augmented generation systems, document assistants, enterprise search, summarisation tools, agentic workflows, tool-using assistants, and domain-specific generative AI applications. Scope depends on available access, observability, data permissions, and the decisions the system supports.
Testing may cover false claims, unsupported claims, fabricated names or citations, source contradiction, inaccurate numbers or dates, temporal errors, entity confusion, omission that changes meaning, incorrect synthesis across sources, and answers that should have expressed uncertainty or abstained.
Factuality asks whether a claim is correct in the relevant world or domain. Groundedness asks whether the claim is supported by the evidence that the system was permitted or expected to use. A claim can be factually correct but ungrounded, or grounded in a source that is itself outdated or incorrect.
Testing should examine source ingestion, permissions, chunking, indexing, retrieval recall, ranking, context assembly, prompt instructions, answer generation, citation mapping, and abstention. Separating retrieval and generation errors helps identify whether remediation belongs in content, search, orchestration, prompting, model choice, or user experience.
No single automated metric reliably replaces human review for all use cases. Automated checks are useful for scale, consistency, regression, exact validation, retrieval diagnostics, and prioritisation. Human domain experts remain important for nuanced factuality, source interpretation, ambiguity, materiality, and high-risk decisions.
No. Testing provides evidence for the selected scenarios, source data, model versions, prompts, tools, languages, users, and operating conditions. Generative systems are probabilistic, environments change, and untested inputs remain possible. Findings should therefore include coverage, confidence, limitations, residual risk, and retesting triggers.
Typical deliverables include an evaluation strategy, risk taxonomy, scenario and prompt set, reference evidence, expected-answer or claim design, scoring rubric, labelled dataset, findings report, failure examples, root-cause analysis, remediation priorities, regression suite, monitoring recommendations, and governance documentation.
Timing depends on the number and criticality of use cases, system access, model and retrieval configurations, languages, domain complexity, availability of authoritative evidence, expert-review capacity, security approvals, test volume, reporting depth, and whether remediation and retesting are included.
Pricing is influenced by scope, scenario volume, model and platform complexity, source preparation, number of configurations, languages, expert annotation, automation, secure-environment requirements, integrations, reporting, remediation, and ongoing regression frequency. Dataconsultant can provide a written estimate after initial scoping.
Useful inputs include intended use cases, user journeys, model and retrieval architecture, prompts and system instructions, source collections, expected answers, known incidents, policies, risk appetite, target languages, release plans, and access to product owners, engineers, domain experts, security, privacy, compliance, and risk stakeholders.
The engagement can use approved environments, access controls, data minimisation, redaction, synthetic or representative test data, retention limits, logging controls, and supplier restrictions. The correct approach depends on the data, jurisdictions, contracts, client policies, and authorised privacy and security review.
Yes. A controlled comparison can evaluate candidate models, retrieval configurations, prompts, or platforms against the same representative scenarios and criteria. Results should account for version, latency, cost, context limits, data handling, explainability, operational fit, and uncertainty rather than treating one aggregate score as sufficient.
Yes. Support can include source-content changes, retrieval tuning, prompt and policy redesign, citation verification, abstention rules, user-interface changes, human escalation, guardrails, observability, incident procedures, regression testing, and training. Accountable client owners approve implementation and residual risk.
Retesting is commonly triggered by model updates, prompt changes, source or index changes, new tools, new languages, new user groups, policy changes, incidents, drift, supplier updates, and major release gates. High-impact systems may also require scheduled sampling and periodic independent review.
Share the application, users, evidence sources, known failures, release stage, and assurance objectives. Dataconsultant can help define a proportionate hallucination testing scope, required client inputs, delivery approach, and commercial estimate.
