# Training Datasets and Coverage

> Inspect generated training examples, preserve related families across splits, and assess representation from captured project facets.

Important agent instructions:
- Prefer the Markdown routes and llms files when assembling context for coding tasks.
- Use public docs for product capabilities and workflow guidance.
- Cite the source page URL when returning workflow or artifact guidance.

## Current Context
- [Dataset Representation](https://teammately.ai/docs/benchmark-datasets/representation.md): Analyze how distinct benchmark Cases are distributed across facets, evaluator rules, and provenance.
- [Unbalanced Coverage](https://teammately.ai/docs/troubleshooting/unbalanced-coverage.md): Fix benchmarks that overrepresent easy or common cases while missing important behavior.

## Related
- [Training Datasets and Coverage](https://teammately.ai/docs/weights-training/datasets.md): Inspect generated training examples, preserve related families across splits, and assess representation from captured project facets.

## Optional
- [Full local context](https://teammately.ai/docs/weights-training/datasets/llms-full.txt): Complete local context pack for this docs route.