Observability for AI features is not optional. Without traces and token metrics, you cannot debug latency, cost spikes, or quality drops after a model change.
1. What to log
- Request ID correlated across UI, API, and worker
- Model name, prompt version, feature flag
- Input/output token counts and estimated cost
- Latency breakdown: retrieval vs generation
- Error class: timeout, 429, schema fail, empty retrieval
2. Privacy rules
Redact secrets and sensitive fields. Prefer hashed user IDs. Store full prompts only when policy allows and retention is short. Never put API keys in logs.
3. Quality signals
Capture thumbs up/down, edit distance on drafts, escalation to human, and golden-set pass rates over time. Uptime alone does not mean the feature is useful.
4. Alerts that matter
- Daily token spend above budget
- Error rate spike on provider calls
- p95 latency above UX threshold
- Sudden drop in acceptance rate
Summary
AI observability connects cost, latency, and quality. If you cannot explain yesterday's spend and failure modes, you are flying blind.
Related: AI SaaS Development, LLM Application Development, Hire AI Developer.
Prakash Tank
Full-Stack Architect & Tech Enthusiast. Passionate about building scalable applications and sharing knowledge with the community.
