Inference engineering

Using metrics to understand problems — a guide to observability data

When a response fails, which metric should you inspect first, and which records should you check next? This example organizes logs, metrics, and traces around the request path, connecting observations to component responsibilities. It demonstrates article formatting rather than an implemented monitoring integration.

2026-10-13Example author

#Observability #SGLang

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Organize information along the request path
Let each signal answer a question
Preserve validation boundaries

Using metrics to understand problems — a guide to observability data — illustrative cover, not measured data

Organize information along the request path

This example engineering note checks how shorter articles read on wide and narrow screens.

Let each signal answer a question

Logs record what happened.
Metrics show changes according to defined measurement criteria.
Traces connect the components traversed by a request.

Preserve validation boundaries

This article is not connected to a monitoring system and displays no randomly generated telemetry, fabricated alerts, or live figures.

Using metrics to understand problems — a guide to observability data

Organize information along the request path

Let each signal answer a question

Preserve validation boundaries

Keep reading

Inference runtime notes — start with the configuration

From request to token — understanding inference infrastructure

Organize information along the request path​

Let each signal answer a question​

Preserve validation boundaries​

Keep reading

Inference runtime notes — start with the configuration

From request to token — understanding inference infrastructure

Organize information along the request path

Let each signal answer a question

Preserve validation boundaries