Prompt engineering, The practice of designing and improving prompts to get better results from AI models., Task context, Background information that helps the AI understand what task it is expected to perform., Clear instructions, Specific and unambiguous directions given to an AI model., Real-world scenario, A practical situation that could occur outside a training or demonstration environment., System prompt, A set of instructions that defines the AI's role, behavior, and knowledge for a task., Background information, Additional details provided to help the AI better understand the task or data., Few-shot prompting, A technique that improves AI performance by providing examples of inputs and desired outputs., Conversation history, Previous interactions that provide context for the current task or response., Step-by-step reasoning, A method where the AI processes information in a logical sequence before reaching a conclusion., Hallucination, A situation where an AI generates information that is incorrect, unsupported, or invented., Make a factual claim, To state something as true based on evidence or available information., Output formatting, The structure or format in which an AI presents its response (e.g., XML, JSON, tables)., Final verdict, The AI's final decision, conclusion, or assessment after analyzing the data., XML tags, Markers used to organize and label information within a prompt, making it easier for AI to process., Extended thinking, A feature that allows the AI to spend more time reasoning through a problem before responding.

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