Prompt
The text instruction you give to an AI model to generate a response. Prompt quality directly impacts output quality — better prompts yield dramatically better results.
Why it matters
The prompt is your steering wheel. The same AI can give you a vague, generic answer or a genuinely useful one depending entirely on how you ask. Most people's disappointing AI experiences come from a one-line, ambiguous request. Learning to write clear prompts is the single highest-payoff skill for getting real value out of any AI tool, and it takes minutes to improve.
In practice
Compare "write a cover letter" with "Write a 200-word cover letter for a junior marketing role at a small nonprofit. I have two years of social-media experience and no degree. Keep the tone warm and confident." The second version gives the AI the role, length, background, and tone it needs, so the result actually sounds like you instead of a generic template you'd have to rewrite anyway.
Related terms
Artificial Intelligence (AI)
The simulation of human intelligence by computer systems, including learning, reasoning, and self-correction. Modern AI is primarily powered by machine learning and neural networks.
Context Window
The maximum amount of text (measured in tokens) an AI model can consider at once. Larger context windows allow the model to reference more information in a single conversation.
Large Language Model (LLM)
An AI model trained on vast amounts of text data that can generate, summarize, translate, and analyze human language. Examples include GPT-4, Claude, Gemini, and Llama.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. Powers chatbots, translation, sentiment analysis, and text summarization.
Token
The basic unit of text that AI models process — roughly 3/4 of a word in English. 'Unbelievable' is 3 tokens. Token limits determine how much text a model can process at once.
Tokenizer
The component that splits text into tokens (sub-word units) before feeding it to an AI model. Different models use different tokenizers — affecting how they count input length, handle multilingual text, and process code.