Build a Structured System Prompt from Scratch

A well-structured system prompt is the single biggest lever for improving LLM output quality. Rather than writing free-form instructions, breaking the prompt into explicit sections — Role, Task, Constraints, Output Format, and Examples — forces you to think through every edge case before the model encounters it in production. This example demonstrates the canonical prompt structure used by teams shipping reliable AI features, with each section clearly labelled and easy to iterate on. The Role section defines the persona and expertise level the model should adopt. The Task section describes the specific job to perform with enough context to resolve ambiguity. Constraints list what the model must never do, which is often as important as what it should do. The Output Format section specifies structure (JSON, markdown, plain text) and length expectations, and a few-shot Examples section shows correct input/output pairs that anchor the model's behaviour. Using the prompt builder, you can fill in each section independently and preview the assembled prompt before copying it into your application. Toggle sections on or off depending on your use case — a simple classifier needs a tight Role and Constraints, while a complex data extraction task benefits from explicit Output Format and multiple Examples.