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Observability Stack Setup: Metrics, Logs, and Traces

Set up a complete observability stack — structured logging, distributed tracing, metrics collection, dashboards, and alerting.

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What this prompt does

This prompt stands up a full observability stack for a [application_type] on [infrastructure]. It implements structured logging in [log_format] with [log_levels], distributed tracing via [tracing_tool], metrics ([custom_metrics]) through [metrics_tool], centralized aggregation with [log_aggregator], dashboards for [dashboard_types], alerting ([alert_rules]), on-call via [oncall_tool], SLO/SLI definitions for [slo_targets], error tracking with [error_tracker], and health endpoints, plus correlation-ID propagation, PII redaction, and telemetry cost control.

The structure works because the three pillars only become useful when they are correlated. By requiring a propagated correlation ID across logs, traces, and metrics, the prompt turns three disconnected data streams into one coherent story for a single request. The PII-redaction and cost-optimization steps are not afterthoughts: high-volume telemetry gets expensive and legally risky fast, so designing redaction and sampling in from the start is what keeps the stack sustainable.

When to use it

  • Standing up observability on a new service before the first incident, not after.
  • Correlating logs, traces, and metrics that currently live in separate, disconnected tools.
  • Defining real SLOs/SLIs ([slo_targets]) instead of vibes-based reliability.
  • Adding PII redaction to logs to meet compliance before an audit forces it.
  • Controlling a telemetry bill that is climbing with traffic on [infrastructure].
  • Wiring [oncall_tool] rotations and [alert_rules] so the right person gets paged.

Example output

You get an implementation plan and config: a structured [log_format] logging setup with [log_levels], [tracing_tool] instrumentation with custom spans, [metrics_tool] collectors for [custom_metrics], [log_aggregator] pipeline config, dashboard templates for [dashboard_types], alert rules matching [alert_rules], [oncall_tool] rotation setup, SLO/SLI definitions, [error_tracker] integration, health-check endpoints, plus correlation-ID propagation and PII-redaction guidance.

Pro tips

  • Make correlation-ID propagation non-negotiable; without it, the three pillars stay disconnected and debugging stays guesswork.
  • Set [alert_rules] against [slo_targets] so you page on user-facing impact, not on every transient blip.
  • Tune sampling for [tracing_tool] early; tracing every request at scale is the fastest way to a shocking bill.
  • Define PII redaction before go-live; retrofitting it after sensitive data lands in [log_aggregator] is painful.
  • Keep [custom_metrics] focused on signals you'll act on; vanity metrics inflate cost and dashboard noise.
  • Start with a few [dashboard_types] and expand; an overloaded dashboard hides the signal it was meant to show.

Frequently Asked Questions

Why is correlation ID propagation emphasized so much?
Logs, traces, and metrics are only powerful when you can tie them to a single request. A propagated correlation ID lets you jump from an error log to its trace to the relevant metrics, turning three disconnected streams into one story. Without it, debugging across services stays guesswork.
How does this keep telemetry costs under control?
It addresses cost in two ways: trace sampling for `[tracing_tool]` and focusing `[custom_metrics]` on actionable signals. High-volume telemetry bills climb fast at scale, so designing sampling and metric selection up front prevents the common surprise of observability costing more than the infrastructure it watches.
What's the difference between an SLO and an alert rule here?
An SLO defines your reliability target, such as `[slo_targets]`, while `[alert_rules]` decide when someone gets paged. The prompt ties alerts to SLO-relevant, user-facing impact rather than every transient spike, so on-call stays meaningful instead of being drowned in noise that trains people to ignore pages.
Does it handle PII in logs?
Yes. The prompt includes PII redaction as a first-class step rather than an afterthought. Defining redaction before go-live matters because once sensitive data lands in `[log_aggregator]`, scrubbing it retroactively is painful and may already be a compliance problem. Build the redaction into the logging layer from day one.
Can I use tools other than the defaults like Prometheus or Jaeger?
Yes, every tool is a variable. Swap `[tracing_tool]`, `[metrics_tool]`, `[log_aggregator]`, `[oncall_tool]`, and `[error_tracker]` for your stack, whether that's Datadog, Honeycomb, Loki, or others. The structure stays the same; only the implementation details for each integration change.
Engr Mejba Ahmed

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Engr Mejba Ahmed

AI Developer · Software Engineer

I'm Mejba — I design and ship production AI systems, automations, and full-stack apps. If you want this turned into a working solution for your team, let's talk.

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