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All over the internet, especially on X, you'll find prompts claiming to make chatbots more effective. When I first came across a collection of prompts from David Max, an AI educator, I was skeptical, which is exactly why I decided to give them a try. The prompts say they could turn Claude into everything from a senior debugging engineer to a performance expert. Here's what happened when I decided to find out.Showing Claude my household expense tracker
(Image credit: Future)I had already created a generic expense tracker that included placeholders for expenses with ChatGPT Work, so I used that and then had Claude review the app four times. Each time, I assigned it a different senior engineering role: full-stack engineer, debugging engineer, frontend engineer and performance engineer.The result was far more revealing than simply asking Claude to “make the app better.” Each persona concentrated on a different set of problems — and occasionally found issues the previous “developer” had missed. Here’s what happened and the prompts I used.1. The senior full-stack engineer built the app
(Image credit: Future)I started by asking Claude to create an interactive household expense tracker that an everyday person could use.The prompt: Build an interactive household expense tracker that an everyday person could use to record and understand their spending. Include the ability to add and delete expenses, assign categories, filter transactions, calculate total spending and display a visual category breakdown.Think like a senior full-stack engineer developing a polished startup MVP. Before writing any code, briefly outline the architecture, data structure and user flow. Then build the working app as a Claude Artifact.Make it responsive and easy to use, but do not spend additional time reviewing, debugging or optimizing the finished code. I will ask other “developers” to handle those stages.Get instant access to breaking news, the hottest reviews, great deals and helpful tips.Claude created a surprisingly polished React app. It arrived with sample transactions, spending totals, category breakdowns, search, filtering and the ability to sort transactions by date. It also saved changes so they would remain available after the app was reopened.At first glance, it looked much closer to a finished product than a rough prototype. There were stat cards across the top, a clean expense form and colored bars showing how much had been spent in each category.2. The senior debugging engineer found some problems
