Prompt Engineering
In plain terms
Crafting inputs to get better outputs from AI models.
Going deeper
Prompt engineering shapes instructions, examples, and structure to steer a model toward accurate, useful responses. Small wording and formatting changes can sizably affect results.
Under the hood
Techniques include few-shot examples, chain-of-thought, role framing, and structured output requests; small wording changes can shift results substantially.
Examples
'Think step by step', system prompts, few-shot examples, output-format constraints.
Think of it like...
Like phrasing a request to a brilliant but literal assistant: how you ask largely determines how well they deliver.