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?
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.
Detailed answer & concept explanation~9 min readEverything 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. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
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 products, models, and research that use this idea.
- Arize 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.
- LangSmith offers run comparison views with per-metric confidence intervals, enabling teams to evaluate deployment candidates against last known good baselines.
- Braintrust provides composite quality scoring across multiple eval dimensions with time-series trending and deployment annotations.
- Anthropic publishes cost per task metrics alongside quality scores for Claude models, establishing the multi-dimensional reporting standard that production dashboards should follow.
- Datadog's LLM Observability product integrates latency, error rate, and token-cost monitoring into a unified dashboard with alerting, demonstrating the infrastructure-level integration pattern.
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?
QThe slice-level regression alert fires for a category that constitutes 2% of traffic. How do you decide whether to act on it?
QHow would you handle the case where the user-signal dimension (thumbs-up rate) disagrees with the model-based quality dimension (LLM-judge score)?
Don't say thisRed flags and common mistakes that signal junior thinking. Click to expand.
Red flags and common mistakes that signal junior thinking. Click to expand.
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.
The night-before-the-interview bullets. Scan these on the way to the call.
Primary sources. Skim if you want the original framing.
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