Prompt Engineering
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.
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Recall Questions
Recall questions ask participants to retrieve and state information they have previously learned or been told. They test memory and knowledge retention rather than understanding or analysis. In training sessions and educational presentations, recall questions at the end of a segment can reinforce key points and check how much the audience has absorbed. While they don't assess deeper comprehension, they are an efficient tool for checking baseline knowledge and reinforcing core facts.
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.odp file extension
An .odp file is a presentation created with LibreOffice Impress or other OpenDocument-compatible applications. Like .ppt files, it contains slides with text, images, effects, and media. The .odp format is part of the open OpenDocument standard, making it vendor-neutral and compatible across platforms. Most modern presentation tools, including Microsoft PowerPoint, can open and convert .odp files.
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Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open standard developed by Anthropic in 2024 and widely adopted in 2025 by OpenAI, Google, and Microsoft. It defines a standardized way for AI agents to connect to external tools, data sources, and enterprise systems — without requiring custom integrations for every connection. MCP acts as a universal interface: an AI agent with MCP support can securely access databases, APIs, document repositories, and business applications using a consistent protocol, regardless of the underlying system. This dramatically simplifies how AI is embedded into complex enterprise environments.
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