Term explanation
Prompt engineering is the practice of crafting and refining the instructions given to an AI system in order to produce better, more accurate, or more useful outputs. A well-engineered prompt provides clear context, specifies the desired format, and sets constraints that guide the model toward the intended result. In the context of presentation tools, prompt engineering determines how effectively a user can instruct an AI to generate the right slide structure, tone, and content — making it a practical skill for anyone working with generative AI tools.
With LIZ AI, much of the prompt engineering happens automatically in the background — so users don't need to master complex instructions. LIZ interprets intent and context to generate presentation content that fits, without requiring technical expertise.
Learn more →Master View in PowerPoint allows presenters to edit the Slide Master — a top-level template that controls the default fonts, colors, backgrounds, and layouts applied across all slides in a presentation. Changes made in Master View propagate automatically to every slide that uses that layout, making it the most efficient way to apply brand guidelines and maintain visual consistency across large presentations. Master View is essential for template creation and company-wide design standardization.
Learn moreThe Slide Sorter view in PowerPoint shows thumbnails of all your slides in horizontal rows.The view is useful for applying global changes to several slides at once. Also it's useful for deleting and rearranging slides.
Learn moreA solution presentation is a structured pitch in which a presenter proposes a specific product, service, or approach to address a client's problem or business challenge. It typically frames the customer's pain point first, then presents the proposed solution and its benefits, supported by evidence or case studies. Solution presentations are central to B2B sales processes and consulting engagements, where building relevance and credibility is critical to winning the deal.
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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