What is Prompt?
A prompt is the input text, question, or instruction given to an AI model (especially a large language model) to guide what it should generate or how it should respond.
The model processes the prompt as context and uses patterns learned during training to predict and produce relevant output, such as text, code, or answers.
Prompts can range from simple questions to detailed instructions that include examples, roles, or constraints, allowing users to steer the model's behavior without changing its weights.
Effective prompting often involves techniques like specifying format, providing few-shot examples, or breaking tasks into steps to improve accuracy and relevance.
Example
Typing 'Summarize the plot of Hamlet in three bullet points' into ChatGPT is a prompt that tells the model exactly what kind of output is desired.
Why it matters
Prompts let anyone control powerful AI systems through natural language, making advanced models usable and adaptable without retraining or coding.
Frequently asked questions
A prompt is the actual input you give the model, while prompt engineering is the skill of designing and refining prompts to get better results.
Related terms
Prompt engineering is the practice of designing and refining text inputs (prompts) to guide AI models like large language models toward producing accurate, relevant, or creative outputs.
Few-shot learning (in prompting) is a technique where a language model is given a handful of input-output examples directly in the prompt to guide it on a new task.
Chain-of-Thought (CoT) is a prompting technique that asks an AI model to generate intermediate reasoning steps before giving a final answer, helping it solve complex problems more reliably.
Fine-tuning is the process of taking a pre-trained AI model and continuing its training on a smaller, task-specific dataset to adapt it for a particular use case.
A system prompt is the initial set of instructions given to an AI model that defines its overall behavior, role, rules, and tone for the conversation.
Zero-shot learning (or zero-shot prompting) is when an AI model completes a task it has never seen examples of during training, relying only on a natural language description of what to do.