AI Monitoring

Introduction

AI apps need extra observability: prompt logs, token usage, latency per provider, hallucination rate, user feedback. Without monitoring you're flying blind.

Beginner analogy: Like adding application logs + APM, but for prompts and completions.

Understanding the topic

Core concepts to understand:

• Log every prompt + completion + metadata.
• Track tokens, cost, latency per call.
• Capture user feedback (👍/👎, edits, regenerations).
• Alert on cost spikes, error rate, latency p95.
• Tools: LangSmith, Helicone, Langfuse, Braintrust.

Syntax reference

Visual workflow / architecture:

Each LLM call ─► OpenTelemetry span
                       │
                       ▼
                  LangSmith / Helicone
                       │
                       ▼
            dashboards · alerts · evals

Real-world use

Helicone processes billions of LLM calls. LangSmith is bundled with LangChain. Braintrust ties evals + monitoring.

Best practices

• Adopt monitoring from day one.
• Privacy-redact PII before logging.
• Alert on cost anomalies — they're your earliest warning.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What metrics matter for AI monitoring?
• Q2. Tools you've used?
• Q3. How do you redact PII from logs?