Prompt
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.
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Prompt Engineering: The Complete Guide
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: The Hidden Instructions Behind AI Assistants
System prompts set the rules before a conversation begins. They're how developers shape model behavior, tone, and capabilities at scale.
In-Context Learning: How LLMs Learn from Examples
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.