LLM Observability: The Complete Guide to Monitoring LLMs in Production
Learn what LLM observability is, why it matters, and how to implement comprehensive monitoring for large language models in production environments.
AI Observability & Monitoring
Supervise AI performance in production with observability, drift detection, and operational monitoring.
30 articles & guides
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Guides & Definitions
What is a Model Monitoring Tool?
Model monitoring tools provide visibility into production ML systems—tracking performance, detecting drift, and alerting teams to issues before they impact business outcomes.
What is AI Monitoring?
AI monitoring is the ongoing tracking, analysis, and interpretation of AI system behavior and performance so teams can detect issues early and keep outcomes dependable.
What is AI Observability?
Full-stack AI observability for engineering, data, and compliance teams. Monitor LLMs, agents, and RAG systems with end-to-end visibility.
What is AI Supervision?
AI supervision is the active oversight of AI systems to ensure they behave safely, predictably, and within enterprise constraints.
What is Data Observability?
Data observability is the ability to understand the health and quality of data flowing through your systems—essential for trustworthy AI that depends on trustworthy data.
What is Human-Centric Model Monitoring?
Human-centric monitoring goes beyond metrics to ensure ML insights are actionable, understandable, and tailored to the humans who must act on them.
What is ML Model Monitoring?
ML model monitoring tracks the health and performance of machine learning models in production, detecting drift, degradation, and issues before they impact business outcomes.
What is Model Degradation?
Model degradation is the decline in ML model performance over time as production conditions diverge from training. Understanding causes and detection methods is essential for maintaining model reliability.
What is Model Drift?
Model drift is when an AI system's performance degrades over time, often silently. Learn how Swept AI detects and prevents drift in LLMs and agents.
What is the Difference Between Observability and Monitoring?
Observability and monitoring are related but distinct concepts in AI/ML operations. Understanding the difference helps teams build effective oversight systems for production models.
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