**DRAFT — pending editorial expansion.** This article is a working draft published as scaffolding for the NINtec content programme. The current version covers the substantive perspective in compressed form; the published version will expand each section to the 2,000+ word depth the topic warrants. Editorial review is required before promotion.
Production Claude deployments without continuous evaluation regress silently between releases. Eval discipline is engineering discipline; it is not optional. This piece covers the eval-harness patterns NINtec deploys across production engagements.
Golden-set construction
Golden sets are curated input-output pairs representing the workload's full distribution — happy path, edge cases, adversarial inputs, regulatory edge cases. Golden-set quality is the throttle on subsequent eval iteration speed; investing in good golden sets early pays back.
Judge-LLM scoring
Generated outputs are scored by a judge LLM against rubrics specific to the workload. The judge can be Claude or another model; the rubric is the operative quality artefact. Judge-LLM scoring captures dimensions that exact-match testing cannot.
Drift monitoring
Continuous evaluation against production prompts. Drift alerts when answer quality, retrieval recall, or agent success rate drops below threshold. Root-cause analysis is triggered automatically; remediation paths are pre-defined.
CI gating on eval-bar
Production prompt changes block on eval-bar parity. Pull requests modifying prompts include eval delta in the description. Releases that drop eval-bar require explicit override with documented rationale.
Production eval is the difference between Claude deployments that survive their second quarter and those that regress silently. NINtec's deployments include eval discipline from architecture phase, not retrofitted.