Prompt Engineering: What Actually Works After Testing 500 Prompts
A structured prompt removes ambiguity and gets you closer to usable output on the first try. Here is the pattern I landed on after a year of testing. I have spent the last year testing AI tools for content work, and prompt engineering is the skill that keeps paying off. Not because prompts are magic, but because a structured prompt removes ambiguity and gets you closer to usable output on the first try. After running easily five hundred prompts through GPT-4, Claude, and Gemini, I have landed on a pattern that works for almost any task. The Structure I Use for Almost Every Prompt My prompts follow five parts, in this order: role, context, task, format, and constraints. The role tells the model who to be. The context gives background it cannot infer. The task is the actual work. The format defines how the answer should look. The constraints set boundaries on tone, length, or what to avoid. Here is a real example I used last week to draft a product comparison page. The role was a senior copywriter who has written for SaaS companies. The context was that the audience is small business owners comparing two invoicing tools, neither technical. The task was to write a 400-word comparison that covers pricing, ease of use, and integrations. The format was plain prose with two subheadings. The constraints were no marketing fluff, no exclamation marks, and no invented features.