#evaluation

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

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.

Evals at Anthropic, OpenAI, and Google aren't afterthoughts. They're gating functions that block releases. Every prompt change triggers the full suite.

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.

3% degradation on summarization? Maybe fine. 3% on code generation? Could break your users. Here's how to set thresholds.

Groq, Cerebras, and other custom silicon promise 10x speed. Here's how to evaluate them without getting burned.