Prompt Engineering
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
Designing effective prompts to obtain better AI outputs: - Clear instructions - Appropriate context - Example guidance - Constraints. Scaffolding role: Effective communication with AI. The quality of prompts determines the quality of outputs; prompt engineering is a core skill in the new era. (Merged: prompt engineering, prompt frameworks, prompt chains)
SCAFFOLDING EFFECT
Reduce cognitive load
Effective communication with AI. The quality of prompts determines the quality of outputs; prompt engineering is a core skill in the new era. (Merged: prompt engineering, prompt frameworks, prompt chains)
Anchor fast decisions
LLMs are conditional generators: the output distribution is strongly shaped by the prompt. Clear instructions, context, examples (few-shot), and constraints together narrow the 'intent space', making the model more likely to sample the target output. Quality depends on translating vague requirements into explicit conditions the model can execute.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Prompt_engineeringverified
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