Term explanation
A large language model (LLM) is an AI system trained on vast amounts of text data that can understand, generate, and transform language at a human-like level. LLMs power a wide range of applications — from chatbots and writing assistants to automated document creation and data summarization. In enterprise software, LLMs are increasingly embedded into workflows to interpret unstructured data, draft content, and translate information between systems automatically. In contrast, the Large Presentation Model (LPM) is a specialised AI system that orchestrates the entire presentation cycle in an enterprise context.
LIZ AI leverages large language models to intelligently compose and update presentation content — turning raw data and context into structured, on-brand slides without manual writing.
Learn more →A .pptm file is a macro-enabled PowerPoint presentation that contains one or more embedded VBA macros in addition to slides with text, images, and formatting. Macros allow presenters to automate repetitive tasks — such as updating data fields or triggering animations — directly within the file. Because .pptm files can run executable code, they are treated with caution by security tools and should only be opened from trusted sources.
Learn moreA speech that is given without any preparation, notes, or cards, is called an impromptu speech. It is often delivered at private events (e.g., weddings or birthdays) or for training presentation skills.
Learn moreThe Theory of knowledge (TOK) presentation is an essential part of the International Baccalaureate Diploma Program (IB). The TOK presentation assesses a student's ability to apply theoretical thinking to real-life situations.
Learn moreAn orchestrator agent is a specialized AI agent that coordinates and directs the work of other agents — rather than executing tasks directly itself. In a multi-agent system, the orchestrator receives a high-level goal, uses task decomposition to break it into subtasks, assigns them to specialist agents, monitors progress, and assembles the final output. This pattern enables reliable automation of complex, multi-step enterprise workflows.
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