Prompt engineering
Prompt engineering is the practice of designing and refining the instructions you give to an AI model so it produces more accurate, useful, and consistent results. It often involves choosing the right wording, adding helpful context, specifying the desired format, and setting constraints (for example, tone, length, or
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Prompt engineering (en-US)
Prompt engineering is the practice of designing and refining the instructions you give to an AI model so it produces more accurate, useful, and consistent results. It often involves choosing the right wording, adding helpful context, specifying the desired format, and setting constraints (for example, tone, length, or what to avoid).
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Common techniques
Typical techniques include: (1) clarifying the goal and audience; (2) providing relevant background or definitions; (3) specifying output structure (bullets, JSON, steps, etc.); (4) using examples (few-shot prompting) to show what “good” looks like; (5) breaking complex tasks into smaller prompts; and (6) iterating—testing prompts, identifying failure modes, and adjusting wording or constraints. For safety and reliability, it also includes steering the model away from unsafe or irrelevant content and verifying outputs when stakes are high.
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FAQ
• How is prompt engineering different from “training” a model? Prompt engineering changes the input instructions at run time, while training updates the model’s parameters. • Does more detail always improve results? Not necessarily—too much or conflicting detail can confuse the model. • What’s a good starting prompt? State the task, include necessary context, and request a clear output format (e.g., “Answer in 5 bullets”).
Client endpoint
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