Evaluation coverage map
A table of core tasks, user groups, failure types, and high-stakes exceptions, with an agreed plan for sampling cases.
Evaluation datasets and benchmarks
We build evaluation sets with reference answers or scoring rules for your application. Cases cover routine tasks, known failures, and exceptions that need separate review.
ScopeA focused benchmark or a maintained evaluation suite
Delivery timingCoverage and reference review determine the first release
How we check itThe handoff documents how cases were chosen, the scoring rules, limitations, and the baseline, so a future score can be interpreted and reproduced.
Know what improved, and what regressed.
We start with the tasks your application must handle and the mistakes it has already made. Each case gets a reference answer or a scoring rubric. Training examples, development tests, and a protected final test set stay separate, so repeated tuning does not inflate the reported score.
Good fit
- Product owners comparing models or preparing an AI release
- Training teams that need an independent check on whether new data helped
Not the right fit
- Teams wanting one overall score to stand in for every use case
- Projects that plan to train and test a model on the same examples
What you get
Reference and scoring collection
Versioned examples with expected outputs or reviewer rubrics, the source of each case, difficulty tags, and scoring instructions.
Protected test and comparison rules
Access rules for the held-out test set, checks that test cases have not leaked into training data, baseline comparison instructions, and a report by task and failure type.
How it works
Identify release decisions
We define which comparisons the evaluation must support and which error types need their own results before deployment.
Write and verify references
We select representative cases, add targeted difficult examples, and check reference answers independently of any model-generated answers.
Lock the evaluation version
We check for overlap with available training material, document scoring uncertainty, and agree on when a new held-out set is needed.
Scope, cost, and ownership
What we need from you
- A target application and the release or model choice it must inform
- Baseline outputs and known failure examples where available
- Access to independent reference material and domain reviewers
What affects cost
- Task breadth and reference-answer difficulty
- Human scoring effort and repeated model comparisons
- Protected test access and scoring automation requirements
Technical scope
- Exact-match, structured validation, rubric scoring, and calibrated model judges by task
- Splits that keep related records together, and overlap checks within the datasets we can access
Support and maintenance
We keep held-out tests out of training, investigate score drift, and version new cases as real application failures appear.
Common questions
Can evaluation examples be synthetic?
Yes, especially for targeted edge cases. We label them as synthetic and keep them distinct from representative real-world examples. Synthetic cases alone do not establish performance in production.
Can you guarantee there is no contamination?
No. We check whether test cases overlap with the datasets and benchmark materials available to the project. We cannot know what an external model saw in pretraining data its developer has not disclosed.