Term explanation
Co-located Audience means that the speaker talks to the audience in person. It is used verbal and non-verbal methods to communicate a message. The speaker makes gestures with their hands, changes their face expression and shows images.
LIZ AI helps teams deliver consistent, on-brand presentations to co-located audiences at scale. Every deck — whether for a small boardroom or a large all-hands — reflects the same accurate data and visual standards.
Learn more →A 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.
Learn moreInterpersonal communication refers to the exchange of information, meaning, and feeling between two or more people. It encompasses verbal and non-verbal signals and shapes relationships in both personal and professional contexts. Effective interpersonal communication involves active listening, empathy, clear expression, and the ability to read and respond to social cues. It forms the foundation of teamwork, leadership, customer relationships, and all forms of collaborative work.
Learn moreAI hallucination describes the phenomenon where an LLM confidently produces content that is factually incorrect, fabricated, or entirely made up — presented as though it were true. Hallucinations occur because language models generate statistically probable text based on training patterns, without access to verified facts. In enterprise contexts, hallucinations in presentations are a serious risk. AI grounding — anchoring outputs to verified company data — is the primary strategy for preventing hallucinations in production AI systems.
Learn moreAI grounding is the process of anchoring an AI system's outputs to verified, real-world data rather than relying solely on knowledge encoded during model training. A grounded AI retrieves relevant, up-to-date information from external sources before generating a response. This significantly reduces the risk of AI hallucinations and ensures that outputs are accurate, current, and contextually relevant — a critical requirement for enterprise AI applications where factual reliability is non-negotiable. Grounding is a core technique used in LLM-powered systems.
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