AI Evaluation & Assurance · Representative evaluation data
Golden Dataset Development
A golden dataset stays credible when cases trace to permitted sources, reviewers share an adjudication rule, critical slices remain visible, and each version has a named refresh owner.
Model scores are only as honest as the cases behind them. We build a versioned set of representative cases and record where each one came from, what behavior is expected, how ambiguity gets handled, who adjudicates disagreement, and who keeps the set current.
You leave with a versioned evaluation case set, calibrated labels, recorded ambiguity decisions, protected splits, known coverage gaps, and a policy for the next refresh.
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Steps, gates, and who decides
How we work
A folder of examples is not a test set. We build this like a product with versions, review rules, and a maintenance owner, because that's what keeps scores comparable six months from now.
Define coverage and rights
We align on the intended use, failure modes, critical slices, candidate sources, usage constraints, and the owner who approves coverage.
- AI assist
- Known failure modes and slices become a draft coverage map for the domain owner to correct.
- Human gate
- Are the cases permitted, relevant, and tied to a real evaluation need? Your domain owner approves which sources and coverage are actually permitted.
Source and structure cases
We collect traceable examples, separate evaluation from tuning material, and design versions and splits around the intended decisions.
- AI assist
- Candidate cases arrive with their source and provenance already tagged, so review is accept-or-reject rather than archaeology.
- Human gate
- Can every case be traced to its source, purpose, and allowed use? Your domain owner confirms each case is traceable and correctly sourced.
Calibrate labels and ambiguity
Domain reviewers apply expected behavior and rubrics, compare disagreements, and adjudicate cases that cannot be resolved by a simple rule.
- AI assist
- Reviewer labels get compared automatically, and the disagreement clusters surface for adjudication.
- Human gate
- Is reviewer agreement sufficient, and are ambiguous cases handled explicitly? Domain reviewers adjudicate the cases a simple rule can't resolve.
Version and hand over
We document coverage, known gaps, contamination checks, refresh triggers, and the workflow for future changes.
- AI assist
- The labeled set turns into a draft coverage report and data card that the owner edits before sign-off.
- Human gate
- Who approves the current version and owns the next refresh? Your domain owner accepts the current version and owns the next refresh.
Named artifacts you keep
What you get
The dataset arrives with the records that explain this version and keep the next one honest.
Playbook
Golden-set evaluation-use and source-rules plan
Defines the intended evaluation use, target tasks, failure modes, slices, source rules, and coverage priorities.
Dataset
Versioned evaluation case dataset and annotation guide
Contains traceable cases, expected behavior, labels, rubrics, reviewer guidance, and protected splits.
Workshop record
Reviewer calibration and ambiguity adjudication log
Records reviewer agreement, disputed labels, ambiguity decisions, and the rationale for adjudicated cases.
Model card
Coverage, provenance, and refresh-policy report
Summarizes coverage, provenance, usage constraints, known gaps, contamination checks, versions, and maintenance triggers.
Scope and honest limits
When to bring us in
This work fits when evaluation examples can't be traced to a source, reviewers label the same case differently, important slices are thin, or tuning material has quietly mixed into the test set.
A good fit when
- Teams compare models, prompts, agents, and releases on different case sets, so score changes cannot be separated from changes in the examples.
- Reviewers label the same ambiguous case differently, but no expected-behavior rule or adjudication log explains which answer should stand.
- Critical tasks and user slices are thin, while nobody owns the refresh that keeps the evaluation set aligned with new failures.
- Cases come from several sources, but no coverage map ties them to intended tasks, failure modes, or the critical slices the score must represent.
- The expected outputs and rubrics exist in reviewer notes, yet disagreement and genuine ambiguity are not calibrated or adjudicated consistently.
- The evaluation cases have versions, but weak provenance and contamination checks leave tuning material able to cross into protected splits.
- New cases are added or retired informally, so no review gate protects comparability when the golden set moves to another version.
Better handled as other work when
- You need legal approval for source rights, privacy, or permitted data use. The dataset records provenance, while your qualified authority makes that judgment.
- You want the golden set to represent every future user, task, or failure. Its coverage holds only for the agreed slices and failure modes.
- You need a large annotation operation or continuous maintenance. This build defines the refresh policy, while ongoing work needs a separate scope.
If one of these is closer to your situation, start here instead: See the evaluation service
We operate the systems we test
It's hard to test a system well if you've never had to keep one running. We operate production AI ourselves, so our evaluation, security testing, and LLMOps work starts from what actually breaks. The people on it are senior engineers, and Zeo has been doing client work since 2011.
Tools we use
Tools behind this work
Confident AI / DeepEvalruns the golden dataset through real evaluation to validate its structure
DVCversions the dataset as the artifact that actually gets handed over
Label Studiorecords reviewer agreement and how disagreement got adjudicated, per case
Tonicfills rare or sensitive coverage gaps with synthetic cases, real data protected
Jupyterruns the source-traceability checks that confirm each case's origin and rights
Scale AIsources and labels cases at scale when internal records don't reach coverage
Adjacent work
Where this leads
Advisory
AI Evaluation Strategy
Coverage priorities here are copied from a failure taxonomy someone already agreed to. When nobody can name which failures matter, there is no basis for deciding which cases to collect.
Assurance
Independent AI Agent Evaluation
A protected split stops being protected the moment the people it tests can see it. Handing the split to an evaluator whose scope the build team cannot set keeps that property.
Assurance
Safety & Policy Evaluation
Misuse cases and their benign lookalikes get adjudicated labels like every other slice here. Which rules those pairs must exercise comes from your written policy, not from the dataset.
Assurance
AI Launch Readiness Review
Every version of this set ships with a refresh trigger and a named owner. A launch review is where an expired version gets caught and written up as a release condition.
Next step
Build a test set that survives the next version
Before you decide