Unsupported claims
Plausible but incorrect statements can enter reports, customer responses or operational decisions.
AI Evaluation & Assurance
Evaluate AI Factuality Before Outputs Inform Business Decisions
DataConsultant evaluates factual claims, evidence use, source faithfulness, citation integrity and uncertainty handling in generative AI and retrieval-augmented systems. The service helps AI, product, data, technology, risk and business teams understand where outputs are dependable, where failure patterns remain, and what controls or remediation are needed before wider deployment or continued operation.
Evidence-based assessment
Risk-focused evaluation
Domain-aware expert review
Practical, actionable recommendations
02.
Why Factuality Evaluation Matters
Generative AI can produce fluent, persuasive outputs that are still incorrect, weakly evidenced or misleading. A factuality programme turns broad concerns about “hallucinations” into specific claims, evidence, severity and repeatable control decisions.
Partial correctness
Some facts may be accurate while omitted conditions or context materially change the conclusion.
Citation defects
References may be missing, misaligned, invented, stale or unrelated to the claim they appear to support.
Conflicting sources
AI may blend incompatible source statements without explaining which evidence is current or authoritative.
Overconfident outputs
High-confidence wording can encourage users to accept uncertain information without appropriate verification.
Stale or weak sources
Outdated references, incomplete retrieval or low-quality content can produce factually weak answers.
Hidden reviewer gaps
Automated scores may miss domain nuance, material omissions or evidence quality issues that require expert review.
Inconsistent behaviour
Similar prompts, model versions or retrieval settings may produce materially different factual outcomes.
03.
From Current State to Target State
Move from ad-hoc output checks to a structured, risk-based factuality assurance programme with repeatable evidence, named ownership and change-triggered re-testing.
Current State
- Ad-hoc spot checks
- Limited test coverage
- Opaque failure handling
- Inconsistent evaluation
- Unclear ownership
- Reactive issue resolution
Target State
- Structured, repeatable evaluation
- Representative test sets
- Traceable evidence and sources
- Risk-based assessment
- Defined release criteria
- Ongoing monitoring and assurance
Assess Your Highest-Risk AI Outputs
Identify factuality risks before unsupported or misleading content affects customers, employees or business decisions.
04.
What Our Factuality Evaluation Service Covers
An end-to-end, risk-based approach can be scoped from evaluation design through remediation, regression testing and operating controls.
Risk Scoping
Identify high-risk use cases, users, claims and decision consequences.
Test Design
Create representative, boundary, ambiguous and evidence-poor scenarios.
Automated Checks
Use repeatable checks to screen groundedness, citation and consistency signals.
Expert Human Review
Validate complex and high-risk claims with domain-aware judgement.
Error Analysis
Identify recurring failure patterns, contributing causes and evidence weaknesses.
Remediation
Prioritise prompt, retrieval, source, workflow and control improvements.
Retest
Validate changes against versioned test cases and agreed acceptance criteria.
Operating Controls
Define monitoring, release gates, ownership and change-triggered review.
05.
Factuality Evaluation Framework
A capability map for assessing claim accuracy, evidence use, uncertainty and release risk. The final dimensions and thresholds are adapted to the intended use and available evidence.
Claim Quality
Clarity, specificity and verifiability of material claims
Evidence Quality
Relevance, credibility, authority and recency of sources
Source Faithfulness
Whether generated content stays faithful to supplied evidence
Citation Integrity
Correct and complete source-to-claim alignment
Contradiction Detection
Identify conflicting claims, evidence and source versions
Abstention & Uncertainty
Appropriate handling of missing, ambiguous or weak evidence
Retrieval Quality
Whether the right context reaches the generation step
Severity & Risk
Business impact if a factual error reaches users or decisions
Regression Coverage
Consistency across versions, prompts and source updates
Governance & Ownership
Clear roles, release criteria, evidence retention and accountability
06.
Illustrative Factuality Evaluation Scorecard
Illustrative scoring only. Actual measures, baselines, thresholds and maturity labels are agreed for the client’s use case and should not be treated as benchmark claims.
| Dimension | Illustrative Score | Score | Illustrative Status |
|---|---|---|---|
| Test coverage | 3/5 | Developing | |
| Evidence traceability | 2/5 | Initial | |
| Citation correctness | 3/5 | Developing | |
| High-severity error handling | 2/5 | Initial | |
| Regression discipline | 3/5 | Developing | |
| Reviewer calibration | 4/5 | Managed | |
| Release-gate maturity | 2/5 | Initial |
07.
Business Priority → Evaluation Requirement Mapping
Link the decision being supported to the required evidence, test method and acceptance condition.
| Business Decision | AI Use Case | Claim Type | Evidence Source | Risk Tier | Test Method | Acceptance Criterion |
|---|---|---|---|---|---|---|
| Customer advice | AI assistant | Factual / external | Approved authoritative sources | High | Automated + human review | No unsupported high-impact claims |
| Internal knowledge | Enterprise Q&A | Factual / internal | Controlled internal documents | Medium | Automated checks | Defined groundedness and citation criteria |
| Market analysis | Research assistant | Factual + analytical | Approved research sources | Medium | Human review | Accurate citations and source use |
| Product content | Content generation | Factual | Approved product sources | High | Automated + human review | No critical factual errors |
| Code / documentation | Technical assistant | Factual / procedural | Versioned product documentation | Medium | Automated checks | Correct and traceable sources |
Align Factuality Tests With Business Risk
Translate customer, operational, regulatory and decision risks into proportionate evaluation coverage and acceptance criteria.
10.
Governance, Risk and Control Model
Clear rules, thresholds, evidence and accountability help turn factuality findings into release and remediation decisions.
Severity Framework
Define impact categories, recurrence factors and escalation logic.
Benchmark Approval
Approve test sets, source collections and version controls.
Evidence Requirements
Set traceability, reviewer notes and source-quality expectations.
Escalation Thresholds
Define when findings need owner, risk or executive review.
Issue Ownership
Assign accountable owners and target actions for material findings.
Retest Evidence
Retain proof that remediation was evaluated against the right version.
Release Decision
Record go, conditional go, restriction, remediation or no-go criteria.
Change-Triggered Regression
Re-evaluate after model, prompt, retrieval, source or policy changes.
11.
Error Prioritisation Matrix
Illustrative matrix only. Actual severity and recurrence bands should be defined against the business consequences of the evaluated system.
Business Impact
InvestigateHigh impact / low recurrence
PrioritiseHigh impact / medium recurrence
PrioritiseHigh impact / high recurrence
MonitorMedium impact / low recurrence
AddressMedium impact / medium recurrence
AddressMedium impact / high recurrence
AcceptLow impact / low recurrence
TrackLow impact / medium recurrence
TrackLow impact / high recurrence
Likelihood / Recurrence →
Prioritisation considers more than a score
- Business and user consequence
- Evidence weakness and detectability
- Frequency, exposure and recurrence
- Downstream decision or automation impact
- Control effectiveness and reviewer visibility
- Remediation complexity and re-test urgency
12.
Factuality Assurance Transformation Roadmap
A practical path from assessment scoping to repeatable operational assurance. Sequence and duration are tailored after discovery.
1. Align & Scope
Objectives & use casesStakeholder interviews and decision contextScope and evidence plan
2. Design Benchmark
Create evaluation datasetCollect, label and validate representative casesTest-set approval
3. Baseline Evaluate
Objective current stateRun automated and human evaluationBaseline scorecard
4. Diagnose Failures
Identify root causesError analysis and pattern detectionPrioritised issue register
5. Remediate
Implement fixesPrompt, retrieval, source, model or workflow changesRemediation log
6. Retest
Validate improvementsRegression and targeted re-evaluationRetest evidence
7. Operationalise
Enable release gatesIntegrate into product and governance processOperating procedure
8. Monitor & Improve
Ongoing assuranceChange-triggered review and periodic samplingMonitoring reports
Build a Practical Factuality Assurance Roadmap
Turn test findings into remediation, re-test, release-gate and monitoring actions that fit your operating model.
13.
Our Delivery Methodology
A focused, outcome-driven sequence designed to produce traceable evidence and practical actions rather than isolated model scores.
Understand
Map goals, system boundaries, intended users and decision consequences.
Risk Scope
Prioritise high-impact factual claims, source paths and control concerns.
Test Design
Create representative, edge, conflicting-source and abstention scenarios.
Evaluate
Run agreed automated checks and calibrated human review.
Review
Validate findings, evidence quality, limitations and material exceptions.
Prioritise
Rank factuality risks by impact, recurrence and remediation need.
Retest
Validate changes against versioned cases and acceptance criteria.
Operationalise
Embed release criteria, ownership, monitoring and regression triggers.
14.
Deliverables and Expected Outcomes
Practical outputs are selected around the decision the evaluation must support and the level of assurance required.
Key Deliverables
- Risk-based test plan
- Evaluation dataset
- Factuality rubric and scorecard
- Claim / evidence findings register
- Error taxonomy and severity rationale
- Issue and remediation register
- Regression test pack
- Reviewer guidance and calibration notes
Expected Qualitative Outcomes
- Clearer release criteria for AI outputs
- Improved visibility of factuality failure patterns
- Stronger source faithfulness and citation integrity
- More disciplined change assessment
- Repeatable reviewer and regression processes
- Traceable evidence for governance decisions
- Defined ownership for material findings
- Practical path to ongoing assurance
15.
Engagement Models and Pricing
Flexible engagement models support a bounded assessment, remediation work, embedded specialist support or ongoing assurance.
- Fixed-scope factuality assessment
- Time-and-materials remediation support
- Consulting retainer or embedded specialist support
- Managed evaluation and regression service
- Dedicated evaluation capability or team
- Training and capability building
Custom Scope & Pricing
DataConsultant does not publish a fixed public price for factuality evaluation. A scoped quote is prepared after the system, risk, evidence and review requirements are understood.
Request a Quote →- Systems, use cases and model versions
- Test volume and languages
- Source and retrieval complexity
- Domain-expert review requirements
- Security and evidence-handling constraints
- Remediation, retest and ongoing assurance
16.
Why DataConsultant for Factuality Evaluation
The service is designed around enterprise decisions, traceable evidence and practical control improvement rather than unsupported claims of perfect AI accuracy.
Risk-based test design
Coverage is linked to actual users, decisions and business consequences.
Claim-level evidence
Findings can connect material claims to supporting, missing or conflicting evidence.
Human + automated review
Repeatable checks are combined with domain judgement where materiality requires it.
Traceable deliverables
Test criteria, findings, limitations, owners and re-test evidence are documented.
Strategy through assurance
Evaluation outputs can be translated into remediation, release gates and operating controls.
Turn One-Time Testing Into Repeatable Assurance
Define re-test triggers, reviewer ownership, evidence retention and release criteria so factuality controls keep pace with model and source changes.
18.
Frequently Asked Questions
Answers to common enterprise questions about factuality evaluation scope, methods, evidence, deliverables, timing, governance and pricing.
What does factuality evaluation test?
Factuality evaluation tests whether material claims in AI-generated outputs are correct, supported by appropriate evidence, faithful to supplied or retrieved sources, cited accurately where citations are expected, and expressed with suitable uncertainty or abstention when evidence is insufficient. The exact rubric is adapted to the business use case, source environment and risk profile.
Which AI systems can be evaluated?
The service can be scoped for generative AI assistants, retrieval-augmented generation systems, enterprise search, copilots, summarisation workflows, research assistants, customer-service applications, document-generation systems, agentic workflows and other AI-enabled processes that produce factual or evidence-dependent outputs.
How do you design the factuality test set?
Test design starts with intended use, user groups, business consequences, source collections, known failure modes and decision requirements. Representative cases are then combined with boundary conditions, evidence-poor questions, conflicting-source scenarios, stale or ambiguous content and higher-risk cases. Test-set size and sampling are confirmed during scoping.
Do you use human reviewers or automated evaluation?
Factuality evaluation can combine deterministic checks, automated evaluation methods and calibrated human review. Human subject-matter review is especially important for ambiguous, domain-specific, high-impact or evidentially complex claims. Automated checks are useful for repeatability and scale but are not treated as a universal substitute for expert judgement.
How is a claim linked to evidence?
Where source access is available, material output claims can be extracted and traced to approved reference content, retrieved passages, authoritative sources or expected-answer evidence. The review can record support strength, citation quality, contradictions, missing evidence, stale evidence and uncertainty so that findings remain auditable.
Can you evaluate RAG factuality and source faithfulness?
Yes. For RAG systems the scope can include retrieval relevance, context coverage, source freshness, answer faithfulness, citation-to-source alignment, unsupported synthesis, contradictions across sources, appropriate abstention and the effect of retrieval or chunking changes on output reliability.
What deliverables are typically provided?
Typical outputs can include a risk-based test plan, evaluation dataset, factuality rubric, evidence register, claim-level findings, scorecard, error taxonomy, severity rationale, root-cause analysis, remediation backlog, regression suite, release-readiness recommendations and monitoring or re-test requirements. Final deliverables are agreed during discovery.
Does a factuality evaluation guarantee that future AI outputs will be correct?
No. AI behaviour can vary with prompts, models, source data, retrieval configuration, tools, policies, user context and software changes. The evaluation provides evidence about the tested scope, identifies limitations and supports risk decisions, but it cannot guarantee the correctness of every future output or eliminate all AI risk.
How are high-risk factual errors prioritised?
Prioritisation can consider business impact, likelihood or recurrence, user exposure, detectability, decision consequence, evidence weakness, affected control layers and whether the error can propagate into downstream actions. Critical unsupported claims in consequential workflows normally receive more attention than low-impact wording differences.
How long does a factuality evaluation take?
Timeline is confirmed after scoping. It depends on the number of systems and use cases, test volume, source complexity, languages, reviewer availability, security constraints, evaluation depth, required remediation cycles and whether regression automation or operating controls are included.
How is factuality evaluation pricing determined?
DataConsultant does not publish a fixed fee for this service. Pricing is scope-led and depends on systems and models in scope, use cases, test volume, source collections, domain-review requirements, languages, evaluation methods, access constraints, reporting depth, remediation support, re-testing and ongoing assurance needs. A written quote follows a defined scoping discussion.
Can the evaluation support governance or audit evidence?
The engagement can produce traceable test criteria, versioned evidence, findings, owners, remediation status, release conditions and re-test records that support internal governance, risk and assurance processes. It does not by itself constitute statutory audit, legal advice, certification or regulatory approval.
What information should we prepare before the engagement?
Useful inputs include the intended use, accountable owner, system architecture, model and prompt versions, retrieval design, approved source collections, representative prompts, expected behaviours, known incidents, logs where appropriate, risk classification, policies, supported languages and access to business or domain reviewers.
Can factuality testing continue after launch?
Yes. Factuality checks can be converted into reusable regression suites and ongoing assurance routines with defined triggers for model, prompt, retrieval, source, policy or workflow changes. Monitoring design can also include sampling, exception review, evidence retention and periodic re-evaluation.
Build Factuality Assurance Your Organisation Can Operate
Share the AI use case, source environment, current concerns and decision you need to support. DataConsultant can help define a factuality evaluation approach proportionate to your risk and operating context.
Risk-based test designClaim-level evidenceHuman + automated reviewTraceable deliverablesOngoing assurance path
Factuality Evaluation Enquiry
Request a Factuality Evaluation Scope Review
Share your contact details and requirement. DataConsultant can review the likely test scope, evidence needs, reviewer involvement and appropriate engagement model.