Prompt Monitoring

Introduction

Monitoring tracks prompts in production: latency, cost, accuracy (via judge), drift, and user feedback.

Beginner analogy: Like APM for prompts — dashboards, alerts, traces.

Understanding the topic

Core concepts to understand:

• Per-prompt metrics: latency, cost, error rate, eval score.
• Sample N% of traffic for LLM-judge scoring.
• User-feedback signals (thumbs up/down) wired to dashboard.
• Drift alerts: eval score drops X% week-over-week.
• Tools: LangSmith, Helicone, Braintrust, Arize, Patronus.

Syntax reference

Visual workflow / architecture:

Live traffic → sample → judge → dashboard → alert
                          ↓
                user feedback

Real-world use

Mature AI teams treat prompt monitoring like APM — Grafana-style dashboards visible to product & engineering.

Best practices

• Sample, don't score everything (cost).
• Set drift alerts.
• Tie user feedback to specific prompts.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What to monitor?
• Q2. Sampling strategy?
• Q3. Drift alerting?
• Q4. Tools?