AI Dashboard

When to use

Use for monitoring AI applications, model providers, agents, evaluations, usage, or operational quality.

When not to use

Do not present invented model metrics, imply quality from token volume, or expose prompts and user data without an approved privacy design.

Inputs and assumptions

Identify operators, decisions, telemetry sources, metric definitions, sampling, retention, privacy constraints, and alert actions.

Workflow

1. Define the operator questions: availability, latency, errors, spend, quality, safety, and workload.
2. Name each metric's source, unit, period, aggregation, and known gaps.
3. Separate system status from model-evaluation quality and human feedback.
4. Provide filters and drill-downs to traces with redaction and access controls.
5. Explain thresholds and alerts; show when data is delayed or sampled.
6. Offer actionable next steps for common incidents.
7. Test aggregation boundaries, missing telemetry, and permission controls.

Decision rules

• Token count or user rating alone does not establish model quality.
• Never expose secrets, private prompts, or customer data by default.
• Distinguish estimated cost from billed cost and indicate currency.
• Show time zone and aggregation windows.

Example

An error-rate chart should define which requests count as errors and link to redacted failure traces; a raw model label is not an explanation.

Common failures

• Unlabeled usage totals and misleading time comparisons.
• Treating model/provider availability as end-to-end product availability.
• Alert panels without ownership or response guidance.

Validation

Verify metric math, time ranges, sampling/retention disclosure, redaction, drill-down permissions, and behavior when telemetry is absent or delayed.