Instrument LLMs and ML models with tracing, metrics, drift detection, and user feedback
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Standardize eval harnesses with golden data, safety tests, and pre-deploy regression gates
Add safety layers to LLMs with content filters, policies, approvals, and audit logs
Instrument LLMs and ML models with tracing, metrics, drift detection, and user feedback
Common questions about ai model observability architecture generator
Prompts, model versions, retrieval context, tool calls, responses, latency, cost, and user feedback with PII controls.
Track feature distributions and output changes over time. Alert on significant shifts and degraded metrics.
Propagate trace IDs through routers, retrieval, tools, and model calls. Visualize spans for latency hotspots.
Add thumbs up/down, reasons, and freeform comments. Store for evaluation and fine-tuning pipelines.
Restrict sensitive logs, redact PII, and enforce retention limits. Provide tenant-isolated dashboards.
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