Dataset Preparation
High-signal benchmark dataset creation, hard-negative mining, deduplication, schema normalization, and multi-annotator agreement rubrics for rigorous AI testing.
What We Prepare
We transform messy production telemetry, domain documentation, and workflow logs into version-controlled, balanced test suites in standardized JSONL formats with verified ground-truth labels.
Why Synthetic Data Alone Falls Short
Fully synthetic datasets generated by standard LLMs share the same blindspots and distributional biases as the models being evaluated. We combine programmatic generation with human domain expert validation and deliberate hard-negative mining to surface authentic error boundaries.
| Quality Dimension | Verification Standard | Impact |
|---|---|---|
| Inter-Annotator Agreement | KAPPA ≥ 0.85 | Guarantees objective scoring criteria on subjective domain evaluation rubrics. |
| Hard-Negative Proportion | 15% – 25% | Prevents models from exploiting surface keyword heuristics to pass evaluation. |
| Near-Duplicate Contamination | < 0.5% | Eliminates repeated samples that artificially inflate benchmark pass rates. |
| Pipeline Stage | Task Count | Filter Type | Retention |
|---|---|---|---|
| Raw Ingested Inputs | 24,000 | Production log dump | 100% |
| Semantic Deduplication | 16,200 | Embedding cluster pruning | 67.5% |
| Hard-Negative Enrichment | 1,800 | Mined boundary cases | Added |
| Final Verified Benchmark | 11,060 | Expert rubric consensus | Verified |
Build an Evaluation Dataset
Connect with our data workflows team to curate a balanced, versioned benchmark suite for your models.