The Evaluation Harness That Keeps GenAI Honest—Before, During, and After Release
If your LLM feature ships without an evaluation harness, you’re not launching a product—you’re running an experiment on your users. Here’s the instrumentation and guardrails that actually work in production.
> You don’t control the model. You control the harness. Invest there.Back to all posts
Key takeaways
- Ship an evaluation harness, not just a model: pre-release tests, during-release gates, post-release observability.
- Instrument LLM calls with OpenTelemetry and export metrics to Prometheus/Grafana; trace prompt and model versions.
- Define SLOs for hallucination rate, latency, and cost; enforce canary gates via Argo Rollouts and circuit breakers.
- Continuously evaluate with golden sets and adversarial tests; automate drift detection on embeddings and content.
- Use guardrails (schema validation, content filters, policy checks) and deterministic fallbacks when violations occur.
Implementation checklist
- Tag every LLM call with prompt_id, prompt_version, model, temperature, and user/session IDs.
- Stand up OpenTelemetry + Prometheus + Grafana for latency, error rate, token/cost, and guardrail violation metrics.
- Create golden datasets and adversarial prompts; run pre-merge evals with RAGAS/TruLens/Promptfoo.
- Release with shadow traffic then canary; gate promotions with Prometheus AnalysisTemplates in Argo Rollouts.
- Implement circuit breakers and kill switches via feature flags; define fallback paths that degrade gracefully.
- Run post-release drift checks with Evidently; alert on embedding and response distribution shifts.
- Version prompts and retrieval indexes; store traces and outputs for replay and root cause analysis.
- Publish an LLM scorecard weekly with SLOs, incidents, costs, and top failing prompts.
Questions we hear from teams
- What’s the minimal viable evaluation harness for a small team?
- OpenTelemetry traces with prompt/model/version + Prometheus metrics for latency, error, cost + a Promptfoo CI eval on a golden set + a LaunchDarkly kill switch. Add RAGAS if you’re doing RAG and Evidently for drift once you have steady traffic.
- How do I measure hallucination in production?
- Sample 1–5% of traffic for human labeling each day, score with RAGAS/TruLens on RAG flows, and track a rolling hallucination rate. Use this as a canary metric in Argo; fail promotion if the labeled sample exceeds the budget.
- Should I build or buy guardrails?
- Start with in-house schema validation and moderation APIs. If your policy surface is complex (regulated domains, tool-use constraints), adopt NeMo Guardrails or similar for policy authoring and auditability.
- What about vendor lock-in on observability?
- Use OpenTelemetry everywhere. Export to Grafana Cloud/Datadog now; you can change backends later. The key is consistent span attributes and metric names.
- How do I control cost without killing quality?
- Track cost per 1k tokens and per request. Cap max tokens, cache with semantic hashing for frequent queries, and route low-risk paths to smaller models. Enforce budget SLOs in your canary gates.
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