Prompt Patterns|

A Pattern Language for Knowledge Engineering with Large Language Models

Structural Segmentation

Separates the prompt into distinct regions so instructions, provided information, and user input are interpreted by their designated purpose.

Problem

Mixed instructions, data, and examples in one stream make role boundaries ambiguous, causing instruction/input bleed and unstable behavior.

Solution

Split prompts into explicit labeled regions (instructions, input, constraints, examples) and isolate untrusted text as data. Role-boundary clarity removes interpretive guesswork and improves containment against prompt-like content in inputs.