What’s the fastest path to lineage if we’re already in production?

Stand up Marquez (or DataHub) in a day, wire OpenLineage to your orchestrator (Airflow/Dagster) and Spark, and add OTel + structured logs to the inference service. Start by emitting `model_version`, `prompt_template`, `corpus_sha`, and `dataset_id`. You’ll get immediate incident response benefits without boiling the ocean.

How do we measure hallucinations objectively?

Use proxies: confidence scoring, citation/grounding rate (answers backed by retrieved documents), refusal rate, and golden set evaluations. Tie these metrics to lineages—prompt version, corpus SHA, model version—so you can attribute changes and roll back the right thing.

Is OpenLineage overkill for a small team?

No. The protocol is lightweight and the Airflow/Spark integrations are low-friction. Even a single service benefits from consistent IDs and a queryable lineage store. You can start small and add custom facets as your pipeline grows.

Where should guardrails live: app, gateway, or model?

All three. Put PII redaction and policy checks in the app layer (cheap), circuit breakers and rate limits in the mesh/gateway, and model-specific constraints (e.g., NeMo Guardrails) at generation. Defense in depth prevents one bad hop from taking you out.

Ai-delivery · Oct 30, 2025 · 9 minute read

Lineage Or Die: The Quiet Control Plane That Keeps Your AI From Lying In Prod

If you can’t trace what trained your model or what fed your last inference, you’re not running AI—you’re running a slot machine. Here’s the instrumentation, lineage, and guardrails that actually work.

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Lineage Or Die: The Quiet Control Plane That Keeps Your AI From Lying In Prod

Key takeaways

Implementation checklist