How do I measure hallucination objectively?

Use a mix: semantic similarity to a reference answer, citation coverage to required sources, and a grounding score (e.g., whether each claim is supported by retrieved docs). Start with thresholds (sim ≥ 0.72, citations ≥ 0.8) and tighten as you learn.

What’s a reasonable SLO set for a first release?

Start with p95 latency ≤ 2.5s, hallucination rate ≤ 3%, error/timeout ≤ 1%, and grounding score ≥ 0.8. Adjust by route and user segment as you get data.

Do I need LangSmith/TruLens/Ragas, or can I homebrew?

You can start with `pytest` + `sentence-transformers` + Prometheus. Larger teams benefit from hosted eval dashboards, experiment tracking, and dataset management. The harness matters more than the brand.

How do I prevent cost blowouts?

Instrument tokens per route, set budgets, and use caching (semantic and response). Enforce context window caps and prefer smaller, faster models on low-risk paths; escalate only on confidence or user intent.

What about provider outages or model regressions?

Treat providers like any dependency. Use Istio/Envoy timeouts, retries, and circuit breakers. Keep at least one fallback model and a cached fast path. Canary provider/model changes behind flags with automatic rollback.

Ai-delivery · Nov 3, 2025 · 8 minute read

Ship GenAI Without Regret: The Evaluation Harness That Keeps Features Accountable

If your genAI feature can’t prove it’s grounded, fast, and safe at every stage, it has no business in production. Here’s the evaluation harness we deploy before, during, and after release.

Back to all posts

Ship GenAI Without Regret: The Evaluation Harness That Keeps Features Accountable

Key takeaways

Implementation checklist