📖The AI monitoring glossary

AI monitoring, in plain English

Clear definitions of the terms behind keeping AI automations safe and accurate. No jargon, no PhD required.

AI Output Monitoring

AI output monitoring is the practice of automatically checking what an AI produces, before that output reaches a customer, system, or decision.

Human-in-the-loop (HITL) means inserting a person to review and approve or reject an AI's output before the automation continues.

AI guardrails are the rules and checks that constrain what an AI is allowed to output, blocking unsafe, off-brand, or incorrect responses.

LLM as a judge uses one language model to evaluate another model's output against criteria you define, such as accuracy, tone, or relevance.

AI evaluation is the process of measuring whether an AI's output meets defined criteria for quality, accuracy, and safety.

AI quality assurance is the discipline of ensuring AI-generated outputs are accurate, consistent, and safe before and after they go live.

An AI hallucination is when a model produces confident, plausible-sounding output that is factually wrong or fabricated.

AI agent monitoring is the practice of overseeing what autonomous AI agents do and produce, so their actions and outputs stay correct and safe.

AI observability is the ability to see what an AI system is doing and producing in production, in order to understand, debug, and trust it.

An AI approval workflow routes an AI's output to a person for approve or reject before the automation acts on it.

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