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Error tracking

As explained in Build AI-Enhanced Web Apps

Error tracking is the collection and analysis of application failures to support diagnosis and resolution.

Error tracking collects and analyzes application failures so teams can identify problems promptly, resolve them, and maintain a stable user experience. section 9.6.2

Scale the practice with application traffic:

  • Use basic error-tracking tools for low-volume applications. section 9.6.2
  • Add comprehensive logging and error tracking at medium scale to capture debugging and auditing information. section 9.6.2
  • Use external providers such as Sentry or Datadog when more customizable tracking is required. section 9.6.2

A production deployment progression

A small application can begin with basic error tracking. As traffic grows, detailed logging and tracking can capture the information needed to debug and audit failures, potentially through Sentry or Datadog. section 9.6.2

How Build AI-Enhanced Web Apps covers it

How deep it goes
Distinguishes basic tracking from detailed logging and tracking but does not explain implementation steps or internals.
Examples
Uses low-volume and medium-scale deployment tiers, with Sentry and Datadog as customizable provider examples.
What you can do after
Diagnose/debug with it by capturing failure information and identifying problems for resolution.
How it connects
Connects error tracking with monitoring, alerting, logging, debugging, auditing, and production deployment.

This explainer was generated by Manning's knowledge graph from the text of Build AI-Enhanced Web Apps. The section links point to the parts of the book it draws on.