The input text sent to a language model, the question, instruction, or context that triggers a response.
A prompt is any text input provided to a language model to elicit a response. Prompts can range from a simple question ('What is the capital of France?') to complex multi-part instructions with context, examples, formatting constraints, and a persona for the model to adopt. The quality and structure of a prompt dramatically affects the quality of the model's output.
Prompts typically consist of several components: a system prompt (instructions about the model's role and behavior), user messages (the actual request), and optionally few-shot examples (demonstrations of desired behavior) and context (retrieved documents, conversation history, etc.).
Prompt engineering, the practice of crafting effective prompts, has become a valuable skill. Techniques like chain-of-thought prompting, role assignment, output format specification, and self-consistency prompting can dramatically improve model outputs on complex tasks without any fine-tuning.
Master the art and science of writing effective prompts, from basic techniques to advanced methods like chain-of-thought, few-shot learning, and structured output generation.
System prompts set the rules before a conversation begins. They're how developers shape model behavior, tone, and capabilities at scale.
How large language models adapt to new tasks from examples in the prompt, without gradient updates or fine-tuning, and what this capability means for AI flexibility.