Evaluation blueprints for GenAI systems

GenAI features fail quietly unless evaluation is baked into delivery. A good blueprint pairs offline evals (checklists, red-team prompts, golden questions) with online signals (satisfaction, refusals, latency, cost) and makes both visible to owners.

Build the evaluation loop

• Test suites: safety, grounding, and policy adherence using curated prompts and labeled outputs.
• Human review: lightweight expert scoring and dispute flows to refine rubrics over time.
• Online signals: live refusal rates, latency/cost envelopes, and business outcomes tied to rollouts.

Delivery integration

• Gate promotions on evaluation deltas, not just model version numbers.
• Capture provenance: prompt version, model endpoint, tool usage, and context windows for every output.
• Keep auditability: log consent state and disable analytics until a visitor opts in.

Related reading

• Follow-up on safety operations: Operating GenAI safety and policy reviews.
• Pillar hub: GenAI in production.
• Case link: Platform guardrails for ML services.

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