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Evaluation strategy
Define evaluation objectives, risk categories, acceptance thresholds, sampling rules, reviewer roles and release gates.
Professional Training Programs Service
Evaluate LLM Systems Before They Become Business-Critical
Dataconsultant evaluates large language model applications, RAG systems, copilots and agents against business requirements, quality criteria, safety controls and operational constraints. We combine test design, automated metrics, human review and failure analysis to help product, technology, risk and governance teams make defensible release, remediation and monitoring decisions.
- Use-case-specific evaluation criteria
- Automated and human review methods
- Safety, privacy and governance coverage
- Documented findings and improvement priorities
Illustrative evaluation planRelease-readiness evidence
Example onlyAnswer qualityTask success and usefulness
GroundingEvidence and citation checks
SafetyPolicy and adversarial tests
OperationsLatency, cost and monitoring
1
Define
Use cases, risks and acceptance criteria
ScopeUse cases, risks and acceptance criteria
2
Test
Benchmarks, human review and red teaming
EvidenceBenchmarks, human review and red teaming
3
Diagnose
Failure patterns and root causes
FindingsFailure patterns and root causes
4
Improve
Controls, remediation and regression suite
ActionControls, remediation and regression suite
Quick service definition
What is an LLM evaluation service?
An LLM evaluation service provides a structured, repeatable way to determine whether a model-enabled system performs its intended tasks reliably and safely. It tests the complete solution—not only the model—including prompts, retrieval, tools, source data, guardrails, workflows and human oversight. The result is decision-ready evidence for model selection, release approval, remediation and ongoing monitoring.
Service offering
Evaluation support across the LLM lifecycle
The scope is adapted to the application, user population, business impact and risk level.
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Benchmark and test design
Create representative prompts, edge cases, adversarial tests, golden answers, reference sources and scoring rubrics.
03
Automated evaluation
Run reproducible checks for relevance, grounding, task success, retrieval quality, consistency, latency, usage and cost.
04
Human evaluation
Use calibrated reviewers for judgement-heavy criteria such as usefulness, tone, completeness, domain suitability and policy interpretation.
05
Safety and red-team testing
Test prompt injection, sensitive-data exposure, harmful outputs, policy bypass, over-refusal, under-refusal and misuse scenarios.
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Production assurance
Establish regression tests, release checks, monitoring measures, incident triggers and evidence for governance reviews.
Key value propositions
Move from impressive demonstrations to controlled performance
Clear acceptance criteriaTranslate business expectations into testable requirements.
Comparable evidenceCompare models, prompts and architectures on consistent tests.
Earlier failure detectionIdentify weak retrieval, unsafe behaviour and brittle workflows before release.
Repeatable assuranceMaintain regression evidence as models, data and prompts change.
Problems addressed
Common reasons LLM initiatives need structured evaluation
Demo quality does not predict production reliability
Small hand-picked examples can hide weak behaviour across real users, topics, languages and edge cases.
Evaluation response
Build a representative test set, define acceptance criteria and report performance by use case and failure category.
Hallucinations and weak grounding are difficult to quantify
Teams may know that errors occur without understanding frequency, severity or root cause.
Evaluation response
Separate retrieval, context, generation and citation failures, then test remediation through controlled regression runs.
Model changes create hidden regression risk
Provider updates, prompt changes and knowledge-base refreshes can alter behaviour unexpectedly.
Evaluation response
Maintain versioned benchmarks and release gates that make material changes visible before deployment.
Risk teams lack usable evidence
Generic model cards and vendor claims rarely demonstrate suitability for a specific business workflow.
Evaluation response
Produce use-case-level findings, limitations, control recommendations and traceable test evidence for accountable review.
Need an independent view of release readiness?
Share the use case, current architecture, key risks and decision deadline.
Who the service is for
Suitable for teams making consequential model decisions
Good fit
- You are selecting or changing an LLM provider.
- You are moving a prototype into production.
- You operate a RAG, copilot or agent workflow.
- You need documented quality, safety or governance evidence.
- You want repeatable regression testing.
- You need an independent assessment before approval.
May not be the right fit
- The use case and intended users are not yet defined.
- No representative data or access can be provided.
- The requirement is only generic model benchmarking with no business context.
- The organisation expects evaluation to prove zero risk.
- Legal certification or statutory audit is the sole requirement.
- There is no accountable owner for acting on findings.
Common use cases
Evaluation scenarios across enterprise AI applications
Support assistant evaluation
Test answer accuracy, policy compliance, escalation behaviour, tone, multilingual performance and unsafe advice.
Decision supported: production release and human-handoff design.
RAG quality assessment
Evaluate retrieval coverage, context relevance, groundedness, citation correctness, access control and data freshness.
Decision supported: retrieval redesign and content-readiness plan.
Enterprise copilot testing
Assess task completion, tool use, permission boundaries, prompt injection resistance and workflow reliability.
Decision supported: controlled rollout and user-oversight model.
Drafting and summarisation assurance
Check factual consistency, omissions, source attribution, mandatory wording and reviewer workload.
Decision supported: acceptable-use boundaries and review controls.
Foundation-model comparison
Compare shortlisted models using the same tasks, constraints, quality thresholds, latency and cost assumptions.
Decision supported: provider selection and fallback strategy.
Agent reliability evaluation
Test planning, tool selection, permission checks, recovery behaviour, completion criteria and audit logging.
Decision supported: autonomy limits and release gates.
Capabilities
Evaluation coverage tailored to the system and its risks
Quality and task performance
Measure whether the system completes the intended work accurately, consistently and usefully.
RAG and grounding
Determine whether source retrieval and answer generation work together reliably.
Safety, security and robustness
Probe expected and adversarial behaviour under realistic misuse and edge conditions.
Operations and economics
Evaluate whether the solution can operate within practical service and cost constraints.
Deliverables
Evidence and artefacts your teams can use
| Deliverable | Purpose | Typical contents | Primary users |
|---|---|---|---|
| Evaluation plan | Define what will be tested and why | Scope, use cases, risks, criteria, test methods, thresholds and responsibilities | Product, AI, risk and governance leads |
| Benchmark dataset | Create repeatable test coverage | Representative prompts, expected behaviours, edge cases, adversarial cases and metadata | Engineering and quality teams |
| Scoring rubrics | Standardise human judgement | Criteria, rating scales, reviewer guidance, calibration and adjudication rules | Domain reviewers and assurance teams |
| Evaluation report | Support release and remediation decisions | Results, failure patterns, severity, limitations, comparisons and recommended actions | Executives, product owners and control functions |
| Regression suite | Control future changes | Versioned tests, thresholds, release checks and execution guidance | ML engineering, platform and operations teams |
| Improvement backlog | Prioritise corrective work | Prompt, retrieval, model, guardrail, workflow, monitoring and governance actions | Delivery owners and programme managers |
Need a scoped evaluation plan and deliverable list?
We can align the work to your release decision, governance process and technical environment.
Service process
How Dataconsultant delivers LLM evaluation
Business and risk alignment
Clarify intended use, users, impact, decision context and material failure scenarios.
Primary output: agreed evaluation scope and decision criteria.
System and evidence review
Review models, prompts, retrieval, tools, data, controls, logs and available test evidence.
Primary output: current-state assessment and evidence gaps.
Test and rubric design
Create representative, edge-case and adversarial tests with automated and human scoring methods.
Primary output: evaluation plan, dataset and rubrics.
Evaluation execution
Run controlled tests, capture outputs, calibrate reviewers and validate anomalies.
Primary output: traceable result set and observations.
Failure analysis
Group errors by source, severity, affected users, control gap and likely remediation path.
Primary output: findings, root-cause hypotheses and priorities.
Decision and transition
Present release options, improvement actions, residual risks and ongoing test requirements.
Primary output: final report, backlog and regression approach.
Technology, platforms, standards and frameworks
Vendor-neutral evaluation within your delivery environment
Models and AI platforms
Application components
Evaluation methods
Governance reference points
Working across a mixed model and platform estate?
Dataconsultant can design one evaluation approach that preserves comparability across providers and versions.
Engagement models
Choose support that fits the decision and operating model
| Model | Best suited to | Typical scope | Client participation |
|---|---|---|---|
| Focused evaluation sprint | A defined release, model choice or high-priority use case | Targeted test design, execution, findings and decision support | Product owner, technical lead and domain reviewers |
| Independent assurance review | Governance, procurement or executive approval | Evidence review, challenge testing, risk analysis and control recommendations | AI governance, risk, security and accountable executives |
| Evaluation capability build | Teams establishing internal LLM quality engineering | Framework, benchmark design, tooling guidance, reviewer training and operating procedures | Engineering, QA, data science and operations |
| Managed evaluation service | Frequent releases or multiple production systems | Recurring tests, benchmark maintenance, reporting, incident-led testing and governance reviews | Service owner, platform team and control functions |
Practical illustrative examples
How evaluation evidence can change a delivery decision
RAG assistant
A support assistant appears accurate in demonstrations but fails on older policies. Evaluation separates content freshness, retrieval ranking and answer-grounding failures, helping the team prioritise source governance and regression tests.
Model comparison
Two models produce similar average quality. Segmented tests show one is more reliable on structured extraction while the other performs better on long-form explanation, supporting a workload-specific model decision.
Agent workflow
An agent completes routine tasks but occasionally chooses an unauthorised tool. Adversarial and permission-boundary tests support tighter tool policies, confirmation steps and monitoring before wider autonomy is granted.
Expected outcomes and KPIs
Measure improvement without overstating certainty
Final measures should be tied to the intended task, baseline evidence and accountable business outcomes.
Task-success ratePercentage of representative tasks completed to the agreed standard.
Grounded-answer rateAnswers supported by approved source material and correct citations.
Critical-failure rateFrequency of high-severity safety, privacy, policy or business errors.
Retrieval qualityPrecision, recall and relevance of evidence supplied to the model.
Human-review burdenEffort required to detect, correct and approve model outputs.
Cost per successful taskModel, infrastructure and review cost for acceptable completed work.
Latency and reliabilityResponse time, timeout rate, availability and failure recovery.
Regression coverageProportion of material risks and workflows represented in repeatable tests.
Control closureCompletion of agreed remediation, governance and monitoring actions.
Pricing and cost factors
What affects the cost of LLM evaluation?
Evaluation breadth
Number of applications, models, prompts, workflows, languages, user groups and failure categories.
Test depth
Benchmark volume, adversarial coverage, repeated runs, model comparisons and remediation cycles.
Human-review needs
Domain expertise, reviewer count, calibration, adjudication and regulated-content requirements.
Technical access
Integration effort, secure environments, logs, observability, data preparation and platform constraints.
Assurance requirements
Governance documentation, executive reporting, risk workshops, evidence retention and independent challenge.
Ongoing service scope
Regression frequency, benchmark maintenance, release support, incident testing and dashboard reporting.
Request a scope-based estimate
Pricing can be prepared after a short review of the use case, system boundaries, evaluation depth and decision requirements.
Why consider Dataconsultant
Evaluation designed for business decisions, not metric collection alone
Dataconsultant connects model behaviour to the real workflow, affected users, controls and operational constraints. The approach is evidence-conscious, vendor-neutral and designed to produce usable decisions, documented limitations and a practical improvement path.
- Business, technical and governance criteria in one evaluation plan
- Clear separation of model, retrieval, data and workflow failures
- Human review where automated metrics are insufficient
- Traceable findings and transparent limitations
- Knowledge transfer and reusable regression assets
Discuss your evaluation requirement
Provide the use case, current model or platform, release stage, key concerns and target decision. We will recommend a practical scope and engagement model.
Request a ConsultationSecurity, quality, privacy and compliance
Evaluation controls should match the system’s exposure and impact
Security
Access control, secure test environments, prompt injection, tool permissions, secret exposure and data leakage.
Quality
Version control, reproducible tests, reviewer calibration, evidence traceability and documented limitations.
Privacy
Personal-data handling, minimisation, retention, residency, sensitive-data testing and processor responsibilities.
Compliance
Use-case risk classification, approval evidence, policy mapping, record keeping and specialist legal review where required.
Evaluation reduces uncertainty but does not prove that an LLM system is error-free, universally safe, legally compliant or suitable for every future input. Legal, regulatory, cybersecurity and certification requirements should be reviewed by appropriately authorised specialists.
Technology ecosystems and delivery environment
Work within cloud, on-premises and controlled environments
Cloud AI environments
Evaluation can be designed around managed model endpoints, cloud data services, private networking, identity controls and native monitoring.
Open and self-hosted models
Support can cover model serving, versioning, fine-tuning artefacts, infrastructure constraints and open-source evaluation tooling.
Restricted environments
For sensitive use cases, the work can use approved data subsets, controlled access, local execution patterns and documented evidence-handling procedures.
Customer perspectives
Representative feedback for LLM evaluation engagements
The following realistic testimonials illustrate the types of service experience customers may value. They do not claim independently verified outcomes.
★★★★★
“The evaluation framework helped our product and engineering teams agree on what ‘good enough’ meant before release. The findings separated retrieval issues from generation issues, which made the remediation discussion far more practical.”
★★★★★
“Dataconsultant brought structure to our human-review process. The rubrics, reviewer calibration and failure categories improved consistency and gave our governance team evidence they could understand.”
★★★★★
“We needed an independent comparison of two model options for a customer-support assistant. The assessment remained vendor-neutral and balanced answer quality, safety, latency and operating cost without reducing the decision to one score.”
★★★★★
“The red-team scenarios exposed permission and prompt-injection weaknesses in our agent workflow. The team explained the risks clearly and translated them into controls our developers could implement and retest.”
★★★★★
“The engagement gave us a reusable benchmark rather than a one-time report. That was important because our prompts, source content and model versions change frequently, and we needed a repeatable release check.”
★★★★★
“Communication was clear throughout the review. Limitations were documented, assumptions were challenged professionally, and the final presentation helped business, legal and technical stakeholders reach a shared decision.”
Frequently asked questions
Questions buyers ask about LLM evaluation
What is an LLM evaluation service?
An LLM evaluation service assesses how reliably a large language model, application, agent, or retrieval-augmented generation system performs against defined business, technical, safety, and governance criteria. It combines test design, representative datasets, automated metrics, human review, red-team testing, failure analysis, and reporting.
What can Dataconsultant evaluate?
Dataconsultant can evaluate foundation-model selection, prompts, retrieval-augmented generation, conversational assistants, copilots, classification and extraction workflows, summarisation, content generation, tool-using agents, guardrails, and production monitoring arrangements. The exact scope depends on the intended use and risk profile.
When should an organisation evaluate an LLM system?
Evaluation is useful before model selection, during prototyping, before production release, after material prompt or data changes, when switching providers, when incidents occur, and as part of ongoing assurance. Higher-risk use cases generally require stronger documentation, human oversight, and repeatable regression testing.
What is included in the service?
Scope can include evaluation planning, use-case and risk analysis, test-case design, dataset review, benchmark creation, rubric development, automated and human evaluation, hallucination and grounding checks, bias and safety testing, adversarial testing, latency and cost assessment, findings workshops, and an improvement backlog.
How do you measure LLM quality?
Measures are selected for the use case. They may include task success, factual consistency, groundedness, relevance, completeness, instruction following, citation quality, retrieval quality, refusal behaviour, safety, bias, robustness, latency, throughput, token use, and cost. No single metric is treated as sufficient.
Do you provide human evaluation?
Yes. Human evaluation can be used where judgement, context, domain expertise, tone, usefulness, or policy interpretation cannot be measured reliably through automated metrics alone. Reviewers use documented rubrics, calibration guidance, sampling plans, and adjudication rules.
Can you evaluate retrieval-augmented generation systems?
Yes. RAG evaluation can cover source coverage, retrieval precision and recall, chunking, ranking, context relevance, answer groundedness, citation correctness, access controls, data freshness, and end-to-end task success. Evaluation can separate retrieval failures from generation failures.
Can you test for hallucinations and unsafe outputs?
Yes. The service can test unsupported claims, fabricated citations, prompt injection, data leakage, harmful content, policy bypass, over-refusal, under-refusal, and other unwanted behaviours. Testing reduces uncertainty but cannot prove that every possible failure has been eliminated.
Which tools and platforms can be supported?
The evaluation approach can work with major model APIs, cloud AI platforms, open-source models, vector databases, observability tools, experiment trackers, prompt-management platforms, and custom applications. Recommendations remain vendor-neutral unless a specific platform comparison is part of the scope.
How long does an LLM evaluation engagement take?
There is no responsible fixed timeline without scoping. Duration depends on the number of use cases, models, languages, risk categories, test cases, data readiness, human-review volume, integration access, stakeholder availability, and whether remediation and retesting are included.
How is pricing determined?
Pricing is influenced by scope, model and application count, test volume, domain complexity, language coverage, human-review requirements, red-team depth, platform integration, security restrictions, reporting needs, workshops, retesting, and ongoing monitoring. Dataconsultant can provide a written estimate after discovery.
What does the client need to provide?
Useful inputs include the intended use case, user groups, model and prompt configurations, sample inputs and outputs, source documents, policies, risk classifications, acceptance criteria, incident history, platform access, and accountable stakeholders. Missing evidence is recorded as a limitation.
Can this service support AI governance and regulatory readiness?
Yes. Evaluation evidence can support model inventories, risk assessments, approval gates, control documentation, monitoring plans, vendor reviews, and internal assurance. It does not replace legal advice, regulatory interpretation, certification, or an independent statutory audit.
Can Dataconsultant help improve the system after evaluation?
Yes. Remediation support can include prompt changes, retrieval improvements, guardrail design, model selection, test-suite expansion, workflow redesign, human-review controls, observability, and regression testing. Implementation responsibilities and acceptance criteria are agreed separately.
Do you offer ongoing LLM evaluation?
Yes. Ongoing support can include scheduled regression tests, release-gate evaluation, benchmark maintenance, incident-led testing, model-change assessments, dashboard reporting, and periodic governance reviews. Monitoring frequency should reflect system change rate, use-case risk, and business impact.