Term explanation
A virtual audience consist of people who join an event / a meeting / a presentation via an electronic device (computer or smartphone) over the Internet. Each member may be located in a different place while an event takes place. Virtual audiences are becoming increasingly important as the amount of events held online is rising.
LIZ AI ensures virtual presentations are polished and current before they go live. It updates data, checks brand compliance, and prepares the deck automatically — so presenters can focus on delivery, not preparation.
Learn more →A vocalized pause is a filler sound — such as 'um', 'uh', 'er', or 'like' — used involuntarily by speakers when they pause to think or gather their thoughts. While occasional pauses are natural, frequent vocalized pauses can undermine a speaker's credibility, reduce clarity, and make a presentation feel less polished. Reducing vocalized pauses is a common goal in public speaking coaching, and is typically addressed through practice, conscious awareness, and the deliberate use of silent pauses instead.
Learn moreClosed questions are questions that can be answered with a limited set of responses — most commonly a simple 'yes' or 'no', or a selection from predefined options. They are used to gather specific, factual information quickly and efficiently. In presentations and training settings, closed questions are useful for gauging audience understanding, confirming agreement, or running quick polls. While efficient, they offer little depth and should be balanced with open-ended questions when richer feedback or discussion is needed.
Learn moreTask decomposition is the process by which an AI agent breaks down a complex, high-level goal into a sequence of smaller, manageable subtasks. The agent identifies dependencies between steps, determines what tools or data each step requires, and decides which subtasks can run in parallel. Task decomposition is a fundamental capability of Agentic AI systems and is central to how an agent loop executes multi-step workflows reliably.
Learn moreA multi-agent system is a setup in which several autonomous AI agents work together, each handling a specific part of a larger task. The agents can communicate, divide work, and combine their outputs to achieve goals that would be difficult for a single model. Typically, an orchestrator agent coordinates the workflow while specialist agents execute defined subtasks. In enterprise contexts, multi-agent systems allow complex workflows — such as researching a topic, drafting content, checking compliance, and distributing a presentation — to be fully automated.
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