Term explanation
An internal preview is a brief statement placed at the start of a new section within a presentation that signals what is coming next. It acts as a mini roadmap within the talk, preparing the audience for the upcoming content and helping them follow the structure. Together with internal summaries, internal previews create a strong narrative skeleton that keeps listeners oriented and engaged, even in presentations that cover multiple distinct topics.
LIZ AI keeps preview slides in sync with live content. When your data or key messages change, internal preview slides are updated automatically — so the overview always matches what follows.
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 moreAI orchestration is the coordination of multiple AI agents, tools, and data sources to complete a complex, multi-step workflow. An orchestration layer acts as a conductor: it decides which agent handles which task, in what order, and how outputs are passed between steps — following the same logic as an orchestrator agent. In enterprise communication, AI orchestration enables end-to-end automation — gathering data, structuring content, applying brand guidelines, and publishing a final presentation — all without human handoffs between each stage.
Learn moreA learning chunk is a small, self-contained unit of educational content covering a single concept or skill. Chunking is a core principle of instructional design: breaking complex topics into manageable segments reduces cognitive load and improves retention. Learning chunks are the building blocks of microlearning programs and modular course structures, and work well in both digital and instructor-led training contexts.
Learn moreThe Model Context Protocol (MCP) is an open standard developed by Anthropic in 2024 and widely adopted in 2025 by OpenAI, Google, and Microsoft. It defines a standardized way for AI agents to connect to external tools, data sources, and enterprise systems — without requiring custom integrations for every connection. MCP acts as a universal interface: an AI agent with MCP support can securely access databases, APIs, document repositories, and business applications using a consistent protocol, regardless of the underlying system. This dramatically simplifies how AI is embedded into complex enterprise environments.
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