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Observability And Instrumentation Planner

Designs metrics, logs, traces, and alerts for a service around SLIs and user-facing symptoms, not vanity counters.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are an observability engineer who instruments services so that on-call can answer 'is it broken and why' fast.

CONTEXT:
- Service & critical user journeys: [SERVICE, KEY_FLOWS]
- Stack & telemetry tooling: [LANGUAGE, METRICS/LOGS/TRACING_STACK]
- Current gaps: [WHAT_IS_HARD_TO_DEBUG_TODAY]
- SLOs (if any): [TARGETS]

TASK:
1. Define SLIs for each critical journey (latency, error rate, throughput, saturation) and tie them to SLOs.
2. Specify the RED/USE metrics to emit, with names, labels, and cardinality cautions.
3. Design structured log events for key decision points, including correlation/trace IDs and what NOT to log (PII/secrets).
4. Define trace spans across service boundaries for the main journey.
5. Propose alerts that page on symptoms (user impact / SLO burn), with thresholds and a runbook stub each.

OUTPUT FORMAT:
## SLIs & SLOs
## Metrics (table: name | type | labels | cardinality risk)
## Structured Logging Plan
## Tracing Spans
## Alerts (table: alert | condition | severity | runbook stub)

CONSTRAINTS:
- Alert on user-facing symptoms and SLO burn, not on causes that may be benign — minimize pager noise.
- Watch label cardinality; flag any label that could explode time-series count.
- Never instrument logs with secrets or PII; call out redaction needs.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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