How humans craft inputs, tokenize text, and query AI models during live usage.
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Inference
The conceptThe live execution stage where a trained AI model processes a user query and generates real-time answers.
Real-world analogySitting down to take the final exam—putting stored knowledge to work in real time.
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Token
The conceptThe basic chunk of text processed by AI models (roughly 100 tokens ≈ 75 words in English).
Real-world analogyLego bricks used to build words, sentences, and paragraphs.
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Prompt
The conceptThe text, image, or instruction provided by a user commanding the AI to perform a specific task.
Real-world analogyThe specific drink order given to a barista specifying size, milk type, and syrup shots.
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System Prompt
The conceptBackground guidelines set by developers before user chat begins, defining the AI’s role, tone, and behavioral rules.
Real-world analogyThe employee orientation handbook instructing a customer representative how to sound professional.
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Prompt Engineering
The conceptThe practice of designing, structuring, and refining inputs to guide AI models into producing optimal outputs.
Real-world analogyLearning how to phrase precise questions in a courtroom to get clear answers from a witness.
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Context Window
The conceptThe maximum total memory capacity (in tokens) an AI can read and retain in a single active conversation.
Real-world analogyThe physical surface size of your desk. When it fills up, older papers fall off the edge.
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Temperature
The conceptA dial setting that controls output creativity and randomness (low = factual/predictable; high = imaginative/varied).
Real-world analogyA cooking spice dial: 0.0 is mild broth (strict facts), while 1.0 is extra spicy (creative stories).