Term explanation
A Large Presentation Model (LPM) is a specialised AI system designed for the demands of enterprise presentation communication. Unlike generic language models, an LPM does not simply process text — it connects company knowledge, audience context, live data, and brand guidelines to produce consistent, presentation-ready communication. The system encompasses the entire presentation cycle: from content structure and storyline to visual execution. Rather than acting as a simple generation tool, it takes on the role of a coordinating, agent-based system.
LIZ AI is the leading Large Presentation Model: deeply integrated into enterprise environments, it connects company knowledge, live data, and brand guidelines to produce consistent, presentation-ready communication.
Learn more →The agent loop is the core operating cycle of an autonomous AI agent. It runs continuously through four phases: Perception (gathering information), Reasoning (planning the next step), Action (executing — such as calling a tool or generating content), and Observation (evaluating the result). The loop repeats until the task is complete or the agent requires human input. This is the mechanism behind Agentic AI systems — it is what allows agents to handle complex, multi-step tasks that a single prompt-and-response model could not.
Learn moreGenerative engine optimization (GEO) is the practice of structuring content and digital presence to improve visibility in responses generated by AI systems — such as ChatGPT, Perplexity, Google Gemini, or Claude — rather than solely optimizing for traditional search engine rankings. Where SEO aims to rank on a results page, GEO aims to be cited inside an AI-generated answer. As AI-generated responses now account for over 60% of all search interactions, GEO has become critical alongside classical prompt engineering strategies for any organization that wants to remain visible in AI-driven search.
Learn moreInstructive Presentations are similar to informative presentations, but it's more than just giving informations. People attend instructive presentations to learn something new and to understand the topic of the presentation better.
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.
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