Term explanation
Generative AI refers to artificial intelligence systems that create new content — such as text, images, code, or structured data — in response to a prompt or task, rather than simply analyzing or classifying existing information. Powered by large language models and other foundation models, generative AI can write documents, summarize reports, produce slide content, and translate data into natural language. In enterprise settings, it is the core technology behind modern AI assistants, document automation tools, and presentation generators.
LIZ AI applies generative AI directly to the presentation workflow: it turns data, context, and company knowledge into structured, brand-compliant PowerPoint slides — automatically and at scale.
Learn more →If there are used images or videos for communication, it is visual communication. Visual Communication is almost used everywhere like on television, posts on social media (Instagram, Facebook), advertisement.
Learn moreTo create your own Template in PowerPoint it is best to use the Slide Master. After updating the Slide Master with your design, all slides (fonts, colours, images, …) adapt to those of the Slide Master.
Learn moreA 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.
Learn moreChain of thought is an AI reasoning technique in which a model explicitly works through intermediate steps before arriving at a final answer. By laying out its reasoning step by step, the model produces more accurate and reliable outputs — especially for complex, multi-part problems. In agentic AI systems, chain-of-thought reasoning is used to plan workflows and make decisions at each stage of an agent loop. For enterprise applications, it increases transparency and makes AI behavior easier to audit.
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