Design a multi-dimensional eval dashboard for a production LLM product. What dimensions, views, and alerts does it need, and what is the one chart that the VP of Engineering should check every Monday?
Design a multi-dimensional eval dashboard for a production LLM product. What dimensions, views, and alerts does it need, and what is the one chart that the VP of Engineering should check every Monday?
Free · 2 AI evals / day
A production LLM eval dashboard needs five dimensions: quality (LLM-as-judge scores, task completion), safety (refusal rate, toxicity, PII leakage), cost (tokens per query, total spend), latency (P50/P95/P99 for time to first token and total), and user signals (thumbs-up rate, escalation rate, repeat-query rate).
Four views serve different needs. A time-series trend shows each dimension over time with deployment markers. A slice-level breakdown shows metrics by query type, catching regressions hidden by stable aggregates. A run comparison shows side by side deltas between current and last known good versions. An alert log tracks fired alerts with resolution status.
**The VP Monday chart** is a composite quality trendline (weighted average of key metrics) with a red/yellow/green band, overlaid with deployment markers and user complaint count. It answers one question: are we getting better or worse?
Four alert types: quality regression (2+ standard deviations from rolling mean), safety breach (immediate, absolute threshold), cost spike (20%+ week over week), and slice-level regression (hidden by stable aggregate).
**Dimensions.** Quality (LLM-judge scores, task completion, format compliance). Safety (refusal rate, toxicity, PII leakage). Cost (tokens per query, judge cost per eval run, total daily spend). Latency (P50/P95/P99 TTFT and total response time). User signals (thumbs-up rate, escalation rate, repeat-query rate, session abandonment).
**Views.** Time-series trend per dimension with model-version deployment markers. Slice-level breakdown by query type, surfacing regressions masked by stable aggregates. Run comparison between current and last known good versions with per-metric confidence intervals. Alert log with resolution status and time to resolution tracking.
**VP Monday chart.** A single composite quality trendline: weighted average of quality, safety, and task-completion metrics, plotted with a red/yellow/green band. Overlaid with deployment markers (vertical lines) and user complaint count (secondary axis). Answers at a glance: are we getting better or worse, and do changes correlate with deployments?
**Alerts.** Quality drop exceeding 2 standard deviations from 30-day rolling mean. Safety metric breach (immediate, absolute threshold, no rolling-mean logic). Cost spike exceeding 20% week over week. Slice-level regression exceeding 3 standard deviations from the slice's own rolling mean, even if the aggregate is stable. The slice alert catches Simpson's paradox in production.
Quick reference
TL;DR
Five dimensions (quality, safety, cost, latency, user signals), four views (trend, slice, run comparison, alerts), composite quality trendline with deployment markers as VP Monday chart.
Imagine the dashboard on the wall of a hospital emergency room. One screen shows vital signs (quality), another shows infection incidents (safety), another shows supply costs (cost), another shows wait times (latency), and a feedback board shows patient satisfaction (user signals). The hospital director checks one chart every morning: a single line showing the overall health score with markers for when new staff started or new equipment arrived. If the line dips after a change, they know where to look. The most dangerous situation is when the overall score looks fine but one department is struggling, hidden by improvements elsewhere.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
An internal tool used by a small team with direct user feedback channels. The full multi-dimensional dashboard is over-engineered when the user base is small enough that direct conversation provides faster and richer signal than automated metrics. A simple quality trend and cost tracker suffices.
A batch processing pipeline with no real-time user interaction. Latency and user-signal dimensions are irrelevant for batch processing. The dashboard can collapse to quality, cost, and throughput. Safety may still apply depending on the output domain.
A prototype in the first week of development. Building a full monitoring dashboard before the product has stable traffic is premature. Start with ad hoc eval scripts and instrument the dashboard when traffic patterns stabilize.
A product where all quality evaluation is done by human reviewers with no automated scoring. The LLM-as-judge quality dimension does not apply. The dashboard would track human reviewer scores, cost, and latency, but the alerting logic changes because human scores arrive with multi-day latency.
Quick scan · 2–4 min
Everything important, quickly.
Open with the five dimensions and why each is independently necessary. Walk the four views: time-series with deployment annotations, slice-level breakdown (stress why aggregate-only monitoring is dangerous), run comparison for deployments, alert log for operational workflow. Describe the VP Monday chart: composite trendline, deployment markers, complaint overlay. Close with the four alert types and why each uses different trigger logic (statistical vs absolute for safety, slice-level vs aggregate).
Real-world examples
Real products, models, and research that use this idea.
1Arize Phoenix provides multi-dimensional LLM monitoring dashboards with per-trace quality, latency, and cost breakdowns, used by production teams to detect regressions across query categories.
2LangSmith offers run comparison views with per-metric confidence intervals, enabling teams to evaluate deployment candidates against last known good baselines.
3Braintrust provides composite quality scoring across multiple eval dimensions with time-series trending and deployment annotations.
4Anthropic publishes cost per task metrics alongside quality scores for Claude models, establishing the multi-dimensional reporting standard that production dashboards should follow.
5Datadog's LLM Observability product integrates latency, error rate, and token-cost monitoring into a unified dashboard with alerting, demonstrating the infrastructure-level integration pattern.
Interviewer follow-ups
What an interviewer would ask next. Try answering before peeking at the approach.
QHow would you compute the composite quality score for the VP chart without making it gameable by teams optimizing one component at the expense of others?
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QThe slice-level regression alert fires for a category that constitutes 2% of traffic. How do you decide whether to act on it?
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QHow would you handle the case where the user-signal dimension (thumbs-up rate) disagrees with the model-based quality dimension (LLM-judge score)?
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QThe dashboard shows a cost spike of 25% week over week, but quality and latency are unchanged. What happened?
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Don't say this
Red flags and common mistakes that signal junior thinking. Click to expand.
Most common mistake
Monitoring only aggregate quality scores without slice-level breakdowns. A 15% regression in one query category can be hidden by improvements in another, making the aggregate look stable while a segment of users experiences real degradation.