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AI evaluation as part of your development cycle
Human-reviewed test sets covering regional data, cultural context, dialects, and real-world scenarios — run at whatever stage of development you need it: pre-launch, ongoing regression, or post-launch monitoring.
Evaluation is not a one-time report — most teams that work with us run it at multiple points: before a launch decision, after a fine-tune to check for regression, and periodically once a model is in production to catch drift. This page covers evaluation as an ongoing solution; for the specific benchmarking methodology and dimensions, see the dedicated model evaluation page.
What this covers
- Pre-launch benchmark testing against your target market and dialects
- Regression testing after fine-tunes or model updates
- Ongoing production monitoring test sets
- Human preference testing between model versions