LLM API Integration Reliability Checklist — 20 Controls Before Production

Most integration failures are predictable.

Use this checklist before calling your LLM API “production ready.”

Request controls

• set explicit timeouts per endpoint
• include idempotency keys for retries
• cap max tokens and tool call depth
• validate JSON schema on all structured outputs

Fallback strategy

• define primary and secondary model routes
• use graceful degradation for non-critical features
• return safe default responses on hard failures

Safety and policy

• run input sanitization (prompt injection defenses)
• enforce output policy filters
• strip or mask PII before requests where required

Observability

Log per request:

• model + version
• latency (p50/p95)
• token usage and cost
• fallback trigger reason
• user-visible error class

Without this, incident triage becomes guesswork.

Evaluation and release

• maintain a golden prompt set tied to business tasks
• run regression checks on every prompt/template change
• block deploys when critical task success drops

Runtime governance

• rate limits by user/org tier
• budget ceilings with alerts
• circuit breaker when error rate spikes
• documented on-call runbook

Incident response

Prepare predefined playbooks for:

• model outage
• degraded latency
• malformed structured output
• policy violation event

Bottom line

LLM API integration is less about a single HTTP call and more about building a resilient system around it.

If your checklist is strong, model changes become manageable instead of existential.