GPT-4o
⚙️ Technical
Intermediate
Observability and Logging Setup
Design a complete observability stack with structured logging, metrics, tracing, and alerting for a production application.
The Prompt
# Observability and Logging Setup You are a site reliability engineer. Design an observability strategy for the following application. ## Application Context Application type: [WEB_API / BACKGROUND_WORKER / MICROSERVICE / MONOLITH] Language and framework: [LANGUAGE_FRAMEWORK] Hosting: [CLOUD_PROVIDER_AND_INFRA] Traffic level: [REQUESTS_PER_DAY] Current observability: [DESCRIBE_EXISTING_SETUP_OR_NONE] ## Observability Design **Structured Logging** - Log schema: what fields every log entry must include - Log levels and when to use each - What to log at request entry, exit, and for errors - Code example in [LANGUAGE] using [LOGGING_LIBRARY] **Metrics** - Key business metrics to track (e.g., order rate, signup rate) - Technical metrics: latency p50/p95/p99, error rate, throughput - Infrastructure metrics: CPU, memory, disk - Metric naming conventions **Distributed Tracing** - Which operations to instrument with trace spans - Context propagation across service boundaries - Recommended tool: OpenTelemetry, Jaeger, or Datadog **Alerting Rules** Write 5 alert rules for [APPLICATION_TYPE] with thresholds and severity levels. ## Dashboard Design Describe the 3 most important dashboards for an on-call engineer.
📝 Fill in the blanks
Replace these placeholders with your own content:
[WEB_API / BACKGROUND_WORKER / MICROSERVICE / MONOLITH]
[LANGUAGE_FRAMEWORK]
[CLOUD_PROVIDER_AND_INFRA]
[REQUESTS_PER_DAY]
[DESCRIBE_EXISTING_SETUP_OR_NONE]
[LANGUAGE]
[LOGGING_LIBRARY]
[APPLICATION_TYPE]
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