AI application quality can drift after launch as user behavior, documents, prompts, and models change. Monitoring helps teams notice issues before users lose trust.
Track User Outcomes
Measure whether users accept answers, retry questions, escalate to support, or abandon the flow.
Log Inputs, Context, and Outputs
Store enough information to debug failures while respecting privacy and retention rules.
Sample Conversations for Review
Human review catches subtle problems that automated metrics miss, especially in high-value workflows.
Watch Cost and Latency
Quality includes speed and sustainability. A great answer that arrives too late may still fail the product experience.
Production AI needs the same discipline as other production systems: monitoring, evaluation, incident response, and continuous improvement.
Frequently Asked Questions
Start with task success or answer acceptance, then add latency, cost, and escalation rates.
Logging is useful, but privacy, consent, and retention policies must guide what you store.
Review prompts whenever failures cluster, source data changes, or the model is upgraded.