#quality

Generic benchmarks don't predict production quality. Domain-specific evals, regression tests, and A/B testing reveal whether your fine-tuning actually worked.

1,000 high-quality examples often outperforms 100,000 noisy ones. Data quality dominates quantity for fine-tuning. Curation is the work.

Models advertise 128K context windows. But attention quality degrades with distance. The last 10% of context often contributes less than the first 10%.

Every configuration lives on a quality-cost curve. Some are on the efficient frontier, most aren't. Map the frontier, then choose your spot deliberately.

Quality regressions are silent killers. Users notice before your metrics do. Automated regression detection catches drops before they become incidents.

LLM judges excel at subjective quality. They fail at factual correctness. Knowing when each applies determines whether your evals are useful or misleading.

If your optimization breaks an eval, the optimization is wrong. Evals are invariants, not suggestions. Ship nothing that fails them.

Bad evals give false confidence. Good evals predict production failures. The difference is designing for the problems users actually hit.

Human review doesn't scale. At 10M responses per day, you're sampling 0.001%. Automated evals are the only path to quality at scale.

Eval suites catch problems benchmarks miss. Here's how to build testing that prevents quantization regressions from reaching users.