AI Follows Instructions
Instructional engineering is about designing model behavior explicitly, not casually chatting with an AI.
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Instructional engineering is about designing model behavior explicitly, not casually chatting with an AI.
A prompt is the instruction structure that defines the model's task and expected behavior.
Templates separate reusable instructions from runtime data.
The model only knows the context you provide during each run.
Prompting is an iterative engineering process based on observing and refining behavior.
An LLM generates outputs by predicting likely continuations of text.
Different models create different tradeoffs in quality, speed, context size, and cost.
Precise constraints reduce ambiguity and increase behavioral consistency.
Output structure determines how reliably systems can use model results.
Assigning roles changes the model's perspective, tone, and depth of explanation.
Delimiters separate instructions from runtime data and reduce ambiguity.
Negative constraints suppress unwanted default model behaviors.