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/english > 37. LLMs & Prompting Vocabulary
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LLMs & Prompting Vocabulary

B1

LLMs & Prompting Vocabulary

Core Terms

TermMeaning
LLMLarge Language Model — AI trained on text (GPT-4, Claude, Gemini)
promptThe input text sent to the model
completion / responseThe text generated by the model
context windowMaximum tokens the model can process at once
tokenA unit of text (~4 characters in English)
temperatureControls randomness: 0 = deterministic, 1 = creative
hallucinationWhen the model generates plausible but incorrect information
fine-tuningFurther training a model on domain-specific data

Prompting Techniques

  • zero-shot: asking the model without examples
  • few-shot: providing 2–5 examples in the prompt
  • chain-of-thought: asking the model to "think step by step"
  • system prompt: instructions that set the model's role and behavior
// TERMINAL CHALLENGE

Проверь себя

Q1. What is a 'context window' in an LLM?
Q2. What is 'hallucination' in the context of LLMs?
Q3. What does 'temperature' control in LLM outputs?
Q4. What is 'few-shot prompting'?
Q5. What is 'fine-tuning' an LLM?
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