Design an analytics dashboard for an AI chat app

Q: Design an analytics dashboard for an AI chat app

This question evaluates a candidate's ability to design scalable observability and analytics systems for an AI chat product, including considerations around instrumentation, ingestion, storage, aggregation, query patterns, dashboard serving, and multi-dimensional slicing by model, region, platform, and tenant.

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Question

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Scenario

Design an analytics dashboard for a ChatGPT-like AI chat product.

The dashboard is used by Product/Engineering/Support to monitor product health and usage.

Requirements

Core use cases

Near real-time monitoring of:
- Usage: DAU/WAU/MAU, sessions, messages per user, retention cohorts
- Performance: end-to-end latency (P50/P95/P99), streaming token latency, time-to-first-token
- Reliability: error rate, timeouts, rate limits
- Quality proxies: user feedback (thumbs up/down), conversation abandonment
- Cost: tokens generated, $ cost by model/tenant
Slice metrics by dimensions: model, region, platform, tenant, app version.
Support time ranges (last 15m, 1h, 24h, custom) and drill-down .

Non-functional

Data freshness targets:
- Operational metrics: ~1–5 minutes
- Business/retention metrics: hourly/daily acceptable
High availability; dashboard should degrade gracefully.
Privacy/security: PII handling, access control, audit logs.

Deliverable

Propose an end-to-end system design: instrumentation, ingestion, storage, aggregation, query patterns, and the dashboard serving layer.

Design an analytics dashboard for an AI chat app

Quick Overview

Scenario

Requirements

Core use cases

Non-functional

Deliverable

Solution

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