05 / Capability

Know where your model can be trusted.

TazQ provides model integrity services: expert human evaluation and red teaming that uncover hallucinations, bias, unsafe behaviour and other failure modes. Specialists test models against explicit criteria and realistic tasks, then turn what they find into clear, repeatable quality signals your team can act on.

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Updated 6 November 2026Evaluation · Red teaming · Hallucination · Bias

Definition

What is model integrity evaluation?

Model integrity evaluation is the structured human testing of an AI model’s reliability and safety. Experts probe the model with realistic and adversarial tasks, judge its answers against explicit criteria, and record where it hallucinates, shows bias, breaks its rules or fails in ways automated benchmarks miss.

Why it matters

Benchmarks don’t catch everything.

The failures that matter most are often the ones automated tests weren’t written to find.

Confident answers can be wrong.

Hallucinations sound fluent and certain. Catching them takes people who know the facts and check the sources.

Bias hides in context.

Unfair or harmful behaviour often appears only in specific situations, languages or phrasings that experts know to test.

Adversaries are creative.

Red teamers think like real attackers, finding jailbreaks and misuse paths before your users do.

How TazQ does it

From uncertain behaviour to clear signals.

Every evaluation follows four steps, shaped around the risks that matter for your model.

01
Define the risks and criteria.
Agree what the model must and must not do, and turn it into explicit criteria and a severity scale.
02
Build realistic and adversarial tests.
Experts write test sets that mirror real use, plus edge cases and attacks designed to break the rules.
03
Evaluate with experts.
Specialists run the tests, judge each answer against the criteria and document every failure with evidence.
04
Report and repeat.
Findings are grouped into a clear report, and test sets are kept so improvements can be measured release after release.

Deliverables

What you receive.

Formats adapt to your evaluation stack. These are the outputs integrity teams ask for most.

Model Integrity data types, what each includes, and typical formats
Data	What it includes	Typical formats
Evaluation report	Findings by category and severity, with examples, likely causes and recommended next steps.	Report with linked evidence
Scored test sets	Prompts and expert-judged responses against your criteria, ready to re-run on new versions.	JSONL with scores and rationales
Hallucination audits	Claims checked against sources, with each unsupported or false statement marked.	Annotated responses
Bias assessments	Behaviour compared across groups, languages and phrasings, with disparities documented.	Comparison tables and examples
Red-team findings	Jailbreaks, misuse paths and policy breaks, with reproduction steps.	Finding log with severity
Failure taxonomy	Your model’s recurring failure modes, defined and ranked to guide training data.	Taxonomy with examples

Use cases

Where teams use it.

01Pre-release evaluation

Independent expert testing before a model or feature launches, focused on the risks that matter most.

02Red teaming

Adversarial testing that finds jailbreaks, unsafe outputs and misuse before real users do.

03Regression tracking

Stable, expert-scored test sets that show whether each new version is better or worse, and where.

04Domain risk reviews

Specialists in fields like health, law or finance checking whether answers meet professional standards.

Quality & experts

Quality starts with people.

Specialist evaluators

Evaluators are matched to each domain and prepared through TazQ Academy on the project’s criteria and severity scale.

Explore TazQ Academy

Explicit criteria

Every judgment follows written definitions and severity levels, so results are consistent and easy to audit.

Evidence for every finding

Each failure is documented with the prompt, the response and the reasoning, so your team can reproduce and fix it.

FAQ

Questions, answered.

What is AI red teaming?

AI red teaming is adversarial testing in which experts deliberately try to make a model misbehave, for example by bypassing its safety rules, extracting harmful content or triggering biased answers, so weaknesses can be fixed before release.

How do you detect hallucinations?

Evaluators check each factual claim in a response against reliable sources, mark statements that are false or unsupported, and note how confidently the model presented them.

Why use human evaluation instead of automated metrics?

Automated metrics are fast but miss subtle errors, context-specific bias and new attack patterns. Expert human evaluation catches these and also produces the labelled examples needed to improve automated checks.

What does an evaluation report include?

Findings grouped by category and severity, representative examples with evidence, likely causes and recommended next steps, plus the scored test set so results can be reproduced.

Can the same tests be re-run on new model versions?

Yes. Test sets and criteria are kept stable and versioned, so each new release can be scored the same way and compared directly.

How do we start an evaluation?

Share your model’s use cases, the risks you are most concerned about and your release timeline. A scoped pilot evaluation then sets the criteria and format before a full programme. You can outline this in the project brief below.

Know where your model can be trusted.

What is model integrity evaluation?

Benchmarks don’t catch everything.

Confident answers can be wrong.

Bias hides in context.

Adversaries are creative.

From uncertain behaviour to clear signals.

Define the risks and criteria.

Build realistic and adversarial tests.

Evaluate with experts.

Report and repeat.

What you receive.

Where teams use it.

Quality starts with people.

Specialist evaluators

Explicit criteria

Evidence for every finding

Questions, answered.

What is AI red teaming?

How do you detect hallucinations?

Why use human evaluation instead of automated metrics?

What does an evaluation report include?

Can the same tests be re-run on new model versions?

How do we start an evaluation?

Let’s build
what comes next.

For AI teams

Know where your model can be trusted.

What is model integrity evaluation?

Benchmarks don’t catch everything.

Confident answers can be wrong.

Bias hides in context.

Adversaries are creative.

From uncertain behaviour to clear signals.

Define the risks and criteria.

Build realistic and adversarial tests.

Evaluate with experts.

Report and repeat.

What you receive.

Where teams use it.

Quality starts with people.

Specialist evaluators

Explicit criteria

Evidence for every finding

Questions, answered.

What is AI red teaming?

How do you detect hallucinations?

Why use human evaluation instead of automated metrics?

What does an evaluation report include?

Can the same tests be re-run on new model versions?

How do we start an evaluation?

Explore further.

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