ce-optimize

Systematic optimization of configuration spaces uses deterministic experiments against a shared measurement harness to evaluate variants, collecting guardrail metrics and preserving the best-performing configurations for deployment.

LLM-as-judge evaluation supports qualitative scoring for search relevance or clustering outcomes, allowing semantic quality assessment when objective hard metrics are insufficient for measuring optimization progress.

Performance tuning begins with a defined hard-spec baseline and optional judge-spec, running serial execution to establish baseline measurements and verify readiness before proceeding with parallel experiments.

Guardrails enforce degenerates gates and maintain an experiment log, while robust state persistence resists data loss across crashes to ensure durability throughout long-running optimization sessions.

This approach applies to performance tuning, clustering, search relevance, and any measurable outcome where iteration and guardrails provide value, adapting to diverse optimization targets through configurable measurement harnesses.

Metric-driven optimization requires a defined optimization spec and measurable outcomes; it is unsuitable when qualitative goals cannot be formalized into either hard metrics or LLM-judge evaluation criteria.