Implementing AI Request Logging and Monitoring in Mobile App
Without logging, AI pipeline is black box. Don't know how many requests each user generates, which prompts give bad answers, where tokens and money grow. AI request monitoring fundamentally differs from regular API monitoring: tokens, cost, latency by phase (TTFT — time to first token), answer quality matter.
What to Log
Minimum per AI request:
Log user_id hashed (not raw — GDPR), session_id, timestamp, model, prompt/completion tokens, total cost, latency, TTFT (streaming), status (success/rate_limited/timeout/content_filtered), fallback_used, cache_hit, guardrail_triggered.
Don't log raw request/response text (confidentiality). Log prompt hash for deduplication and request category (classified by separate model).
Cost Monitoring
AI requests are direct costs scaling with users. Without monitoring, cost unexpectedly 10x on viral growth. Need alerts:
- Daily cost > X USD → Slack/PagerDuty alert
- Cost per user > Y USD → abuse flag
- Average prompt size > Z tokens → context management regression
LangSmith (from LangChain) and Helicone — managed AI observability platforms, integrate in lines of code, provide dashboards out-of-box.
Answer Quality
Latency and cost — technical metrics. Answer quality — business metric. Collect:
- Explicit feedback: thumbs up/down in UI
- Implicit: user rephrased question (repeat request within 10s — answer likely unsatisfying)
- LLM-as-judge: auto quality scoring by separate model per relevance and completeness criteria
Timeline Estimates
Basic logging via Helicone or LangSmith — 1 day. Custom system with PostgreSQL and Grafana dashboards — 2–3 days. With LLM-as-judge and business quality metrics — 3–5 days.