The Purpose of an Eval Dashboard

An eval dashboard is not a data display tool. It's a decision-making tool. Its purpose is to answer a specific question for each stakeholder: "Should we take action?" If the answer is "no action needed," the dashboard failed.

Many teams build dashboards that look like beautiful data warehouses: 20 metrics, 50 visualizations, all updatingin real-time. Then nobody looks at them. Or everyone looks but doesn't know what to do with the information. These dashboards optimize for completeness, not for decision-making.

The best eval dashboards are sparse. They show exactly what someone needs to know to make a decision, and nothing more. A dashboard for an engineering lead might show three KPIs and one alert. A dashboard for an executive might show two metrics and a trend. Less is more.

Audience-First Design

Your first question should be: Who is looking at this dashboard? Different stakeholders need different information and take different actions.

The Engineering Lead's Dashboard

Engineers care about:

Action: Should I rollback? Should I scale? Should I investigate?

The Product Lead's Dashboard

Product managers care about:

Action: Should we expand rollout? Should we pause and fix? Should we target this feature to specific segments?

The Executive's Dashboard

Leadership cares about:

Action: Should I allocate more resources? Should I escalate? Should I pause this project?

Notice: These three dashboards show different metrics, different granularity, and different time scales. Engineering wants 1-hour windows. Product wants 1-day windows. Executives want 1-week or 1-month windows. One dashboard doesn't fit all audiences.

Essential Dashboard Components

Regardless of audience, most eval dashboards need these core components:

1. Primary Metric (Big, Easy to Read)

The one number that matters most. For a chatbot eval: "Task Completion: 87%". Display it big, in the top-left. Include the baseline and the target so context is immediate. "87% (baseline: 76%, target: 85%)".

2. Trend Line (Is it Getting Better or Worse?)

Plot the primary metric over time (last 7 days, last 30 days, depending on audience). A line chart or area chart. Shows direction and volatility. Are we trending toward the target? Away? Stuck?

3. Current vs. Baseline

Side-by-side comparison showing whether the current version is beating the baseline. Visualize as bars or a delta indicator. "87% (↑11pts from baseline)".

4. Segment Breakdown

Performance by segment that matters: language, user type, device, geography. Usually a table or stacked bar chart. "Performance by language: English 91%, Spanish 78%, Mandarin 72%". Where are we failing?

5. Failure Distribution

Categorize failures: "Why did the model get this wrong?" Show a pie or bar chart of failure categories. "Failures: 45% hallucination, 30% context miss, 15% language gap, 10% edge case". This drives improvement priorities.

6. Recent Regressions (Alert Strip)

Highlight any recent drops in performance. "Performance dropped 3 points on Tuesday". Link to the cause if known (new version deployed, data distribution shifted). Engineers need to know what changed.

All six components should fit on one screen (or one scroll on mobile). If your dashboard needs multiple tabs or sections, you've included too much.

Information Hierarchy: Above vs. Below the Fold

The most critical design decision is what to show above the fold (without scrolling) and what to put below.

Above the Fold (Required)

Below the Fold (Optional Deep Dives)

This separation is critical. A busy stakeholder spends 10 seconds looking at your dashboard. If they see the primary metric and status in that 10 seconds, the dashboard worked. If they need to hunt for key information, you failed.

The Three Dashboard Anti-Patterns

Anti-Pattern 1: The Vanity Board

What it is: A dashboard that shows only good news. "Look how well our model is doing!"

Why it fails: If everything looks perfect, stakeholders don't trust the dashboard. They assume you're cherry-picking metrics or hiding problems. Nobody acts on good news anyway; they act on problems.

Fix: Include failure modes and problem areas prominently. "87% overall but 65% on Spanish-language queries." Honesty builds credibility.

Anti-Pattern 2: The Firehose

What it is: A dashboard with 30+ metrics, multiple tabs, overwhelming visualization. Looks impressive but nobody knows what to do with it.

Why it fails: Cognitive overload. Too many metrics = no clear decision. Is 67% CPU utilization a problem? Is a 2-point drop in accuracy significant? The dashboard doesn't answer these questions because there's too much noise.

Fix: Pick 3-5 core metrics per audience. Everything else goes in an appendix. Sparse dashboards drive faster decisions.

Anti-Pattern 3: The Snapshot-Only

What it is: A dashboard that shows only current performance, with no historical context or trend line.

Why it fails: You can't tell if you're improving or degrading. Is 87% good? Who knows! Compared to what? Yesterday? Last month? Without context, the metric is meaningless.

Fix: Always show: current value, baseline, target, and trend over time.

Real-Time vs. Periodic Dashboards

Two different dashboard philosophies for two different purposes:

Real-Time Dashboards (Updated Continuously)

When to use: Production monitoring. You need to know NOW if something is broken.

Update frequency: 1-minute to 1-hour windows

Metrics to show: Immediate health indicators (error rate, latency, availability). Not suitable for slow-moving eval metrics.

Who watches: On-call engineers, SREs, incident response teams

Periodic Dashboards (Updated Daily/Weekly)

When to use: Eval performance trends. You need to know if the model is improving or degrading over time.

Update frequency: Daily or weekly rollups

Metrics to show: Eval scores, segment breakdown, failure rates. Better for human-reviewed data.

Who watches: Product leads, eval teams, decision-makers

Most organizations need both. Real-time dashboards for infrastructure health. Periodic dashboards for model quality and business impact.

Visualizing Uncertainty in Metrics

A metric like "87% accuracy" is misleading without uncertainty bounds. Did you evaluate on 100 examples (high uncertainty) or 100,000 examples (low uncertainty)?

Show Confidence Intervals

Rather than: "Task Completion: 87%"

Show: "Task Completion: 87% [84% – 90%, 95% CI, n=2,340]"

This tells the viewer: the point estimate is 87%, but the true value could be anywhere from 84-90% with 95% confidence. Based on 2,340 examples. This allows them to judge whether differences are meaningful.

Indicate Sample Size

Display sample size prominently. "Performance based on: 2,340 test examples" or "Daily rolling average of 12,000 user interactions". Small sample = less confident. Large sample = more reliable.

Flag Significance

If comparing two versions, flag whether the difference is statistically significant. "Version B: 89% (vs. Version A: 87%, p=0.023, significant)". Don't let stakeholders chase noise.

Alert Design and Avoiding Alert Fatigue

Good alerts are rare and actionable. Bad alerts are frequent and vague ("Check the dashboard")

Alert Strategy

Sample Alert Thresholds

ALERT CONFIGURATION
================================================================================

Yellow Alert (Investigate):
  - Task Completion drops below 85% (vs. target 90%)
  - False Positive Rate exceeds 12%
  - Latency (p95) exceeds 3 seconds

Red Alert (Escalate/Rollback):
  - Task Completion drops below 80% (critical threshold)
  - False Positive Rate exceeds 15%
  - Latency (p95) exceeds 5 seconds
  - Error Rate exceeds 2%

No Alert (Normal Variance):
  - Daily fluctuations of ±2 points
  - Temporary spikes that resolve within 1 hour
  - Variation within confidence interval

Connecting Eval Scores to Business Metrics

The disconnect between eval teams and business is that eval teams care about metrics like "87% accuracy" while business cares about outcomes like "user satisfaction" and "revenue impact".

The best dashboards show both, side-by-side.

Dual-Axis Dashboard

Left axis: Eval metric (accuracy, recall, etc.)

Right axis: Business metric (customer satisfaction, conversion rate, churn reduction, cost saved)

Show both over time on the same chart. This reveals the correlation: Does improved accuracy actually drive better business outcomes? If not, maybe you're optimizing the wrong metric.

Correlation Analysis Panel

Calculate correlation between eval metrics and business outcomes. "For every 1-point improvement in accuracy, we see a 2.3-point improvement in customer satisfaction." This justifies investment in improving eval scores.

Dashboard Tooling Options

Different tools are best for different use cases and teams.

Tool Best For Learning Curve Cost Real-Time Capability Grafana Infrastructure/real-time metrics, open-source friendly Medium Free (self-hosted) Yes, 1-min windows Datadog Comprehensive monitoring, logs + metrics + traces Medium $$$ Yes, 1-min windows Tableau / PowerBI Business intelligence, beautiful visualizations Medium-High $$ No, periodic updates Weights & Biases ML-specific metrics, experiment tracking Low $ - $$ Partial, real-time API Arize AI / Evidently Model monitoring and drift detection Low $$ Yes, streaming data Google Sheets / Looker Simple dashboards, integrates with GCP Low $ Via API polling Custom (Python/React) Highly customized, integrated with ML pipeline High Engineering time Yes, full control

Recommendation for eval teams: Start with Weights & Biases or Arize AI if you're ML-heavy. Start with Grafana if you're infrastructure-heavy. Use custom dashboards only if you have specific requirements that off-the-shelf tools don't meet.

Best Practice

Build dashboards iteratively. Start with a minimal viable dashboard showing just the primary metric. Get feedback from actual users (engineers, product leads). Add more complexity only if people ask for it. Most eval dashboards are over-engineered from the start.