Treat LLM evaluation as infrastructure by establishing structured datasets, human review pipelines, and continuous monitoring to enable defensible, risk-aware AI deployment.

Relying on automated scoring alone isn’t enough; integrate human review to assess contextual judgment, tone, policy-sensitive reasoning, and to catch issues like hallucinations, instruction drift, and edge-case refusals.

Design evaluation datasets to reflect real inputs—ranging from routine queries to complex or adversarial prompts—and have domain experts annotate them to create an auditable ground-truth reference.

Define explicit operational performance criteria aligned with deployment tasks, including factual accuracy, instruction adherence, policy compliance, and contextual reasoning, with task-specific datasets matched to real usage.

Implement lifecycle governance with continuous evaluation as models are retrained or face distribution shifts, and feed evaluation outputs into release approvals, retraining decisions, and risk reviews.

Operate QA with calibrated reviewer sessions and dashboards to ensure consistent scoring across model versions, producing structured documentation for audits and longitudinal tracking.

Embed ongoing LLM evaluation as a governance function across the model lifecycle, not a one-off pre-deployment test, to steadily reduce enterprise AI risk.