Backend Engineering
AI accelerates backend engineering tasks across the full service lifecycle — from designing clean APIs and setting up observability to tuning performance and navigating incident response. This section covers practical workflows for each, including how to critically evaluate AI-suggested patterns against your specific system constraints.
In This Section
API Design
Using AI to design RESTful and GraphQL APIs — endpoint naming, request/response schemas, versioning, and error conventions.
Observability
AI-assisted observability setup — generating logging instrumentation, structured log formats, metrics definitions, and alerting rules.
Performance Tuning
Using AI to analyze slow queries, identify bottlenecks, and suggest optimizations — with verification steps before applying changes to production.
Incident Response
How AI assists during incidents — parsing logs, suggesting hypotheses, drafting runbooks — and the limits of AI judgment under pressure.