# Define SLOs and alerts that only page for real problems

Sets service level objectives and turns them into alert rules that catch user-facing problems without waking people up for noise.

- Topic: Software Development
- Framework: Few-Shot
- Level: Standard
- Tags: Finance, Fill-in Template
- Source: https://www.thinkwithprompt.com/prompts/define-slos-and-alerts-that-only-page-for-real-problems

## Prompt

```text
Help me define SLOs and alerting for a service.

**Service and its users:** [WHAT IT DOES, WHO DEPENDS ON IT]
**Critical user journeys:** [e.g. "log in", "load dashboard", "submit payment"]
**Current alerts:** [PASTE OR LIST - and which ones are noisy]
**Historical performance:** [AVAILABILITY, LATENCY PERCENTILES - or "unknown"]
**Monitoring tool:** [e.g. "Prometheus", "Datadog", "Grafana Cloud"]
**On-call setup:** [WHO GETS PAGED, TEAM SIZE]

Steps:
1. For each critical journey, define an SLI (what we measure, at which point, good vs. bad event) and propose an SLO target and window (e.g. 99.9% over 28 days). Explain the trade-off of the target; don't default to "five nines". If I gave no historical data, mark targets [VALIDATE against 2-4 weeks of data].
2. Calculate the error budget in plain terms (e.g. minutes of downtime or number of failed requests per window).
3. Design burn-rate alerts: a fast-burn alert that pages and a slow-burn alert that creates a ticket, with the windows and thresholds explained.
4. Review my current alerts: keep, convert to a ticket, convert to a dashboard, or delete. Every page should be urgent, actionable, and user-facing.
5. Suggest an error budget policy: what the team does when the budget is spent.

Return: an SLO table (journey, SLI, target, window, budget), the alert rules in my tool's syntax where possible, the alert review table, and a one-paragraph explanation for product stakeholders.
```

## What to fill in

- WHAT IT DOES, WHO DEPENDS ON IT
- e.g. "log in", "load dashboard", "submit payment"
- PASTE OR LIST - and which ones are noisy
- AVAILABILITY, LATENCY PERCENTILES - or "unknown"
- e.g. "Prometheus", "Datadog", "Grafana Cloud"
- WHO GETS PAGED, TEAM SIZE
- VALIDATE against 2-4 weeks of data

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