Term explanation
A .pot file is a legacy PowerPoint template format used to define reusable styles, layouts, and formatting for presentations. Like its successor .potx, it allows teams to create multiple presentations that share the same visual identity without starting from scratch each time. The .pot format was replaced by .potx in Office 2007, which introduced an open XML-based structure for improved compatibility.
SlideLizard CREATOR modernizes the concept of .pot templates: instead of static files, your team always accesses the latest brand-approved templates from a centrally managed slide library directly in PowerPoint.
Learn more →Flipped Classroom means that students work out the subject matter themselves at home through tasks such as reading, videos, etc. Interactive learning activities and exercises then take place in class.
Learn moreComputer-based training (CBT) refers to self-paced educational programs delivered through software on a computer, without requiring an internet connection or live instructor. CBT packages typically include multimedia content, simulations, and assessments. This format was widely adopted in the 1990s and 2000s as an alternative to classroom training, offering consistency and repeatability. Today, CBT is largely succeeded by web-based training (WBT), though it remains in use where internet access is limited or security is a concern.
Learn moreAI hallucination describes the phenomenon where an LLM confidently produces content that is factually incorrect, fabricated, or entirely made up — presented as though it were true. Hallucinations occur because language models generate statistically probable text based on training patterns, without access to verified facts. In enterprise contexts, hallucinations in presentations are a serious risk. AI grounding — anchoring outputs to verified company data — is the primary strategy for preventing hallucinations in production AI systems.
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.
Learn more
