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set-up-drift-alerts

Set up drift alerts for LLM apps using OpenTelemetry GenAI semantic conventions.

29|8|Updated Jul 5, 2026
Search Tags:#drift detection#machine learning#alerting#performance monitoring#LLM observability
One-click install
npx skills add https://github.com/ContextJet-ai/awesome-llm-observability --skill set-up-drift-alerts
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: set-up-drift-alerts
Source: https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/set-up-drift-alerts
Command: npx skills add https://github.com/ContextJet-ai/awesome-llm-observability --skill set-up-drift-alerts

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps catch LLM app drifts in production, alerting on changes in quality, cost, latency, and inputs that could indicate degradation.

Core Features & Use Cases

  • Quality Drift Alerting: Monitor changes in model performance like faithfulness and relevance.
  • Cost Drift Alerting: Detect cost creep from changes in tokens or spend.
  • Latency / Error Drift Alerting: Identify changes in latency or error rates.
  • Input Drift Alerting: Monitor input distribution changes.
  • Use Case: Set up alerts for a production LLM app to ensure its performance remains consistent with baseline metrics.

Quick Start

Set up drift alerts for your LLM app by running the 'set-up-drift-alerts' command and providing necessary monitoring metrics.

Frequently Asked Questions about set-up-drift-alerts

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I monitor LLM app performance drift in production?▼

To monitor LLM app performance drift in production, you can set up drift alerts that track changes in quality, cost, latency, and input distribution to catch degradation early.

What is drift detection for machine learning observability?▼

Drift detection for machine learning observability is the process of monitoring production data, such as LLM inputs and performance metrics, to identify shifts from baseline behaviors.

How do I set up alerts for LLM cost and latency drift?▼

You can set up alerts for LLM cost and latency drift by running the set-up-drift-alerts command, which monitors token spend and response times against your baseline metrics.

Can I use OpenTelemetry GenAI semantic conventions for LLM alerting?▼

Yes, this alerting setup utilizes OpenTelemetry GenAI semantic conventions alongside online evaluation scoring to generate meaningful alerts for performance drift.

Does input drift alerting monitor changes in user prompts?▼

Yes, input drift alerting monitors input distribution changes, detecting when the nature of user prompts or data fed into your LLM app shifts significantly from the baseline.

What metrics should I track to catch LLM quality drift?▼

To catch LLM quality drift, you should track online evaluation scores for metrics like faithfulness and relevance, alerting you when model performance degrades from baseline.

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