I Gave an AI the Same 20 Coding Tasks With Short and Detailed Prompts — More Context Didn’t Always Produce Better Code
There is a habit I picked up after using AI for coding for a while. Whenever the generated code was not quite right, I added more context. Then a little more. Input formats, edge cases, performance requirements, error handling, preferred architecture, things the function should not do. Eventually a two-line request could turn into a small technical specification.It feels logical. A developer cannot read your mind, so why should an AI model be able to? More information should remove ambiguity and give better code. But after a few cases where a detailed prompt produced something strangely overengineered, I started wondering whether this assumption was actually true for small programming tasks.So I made a small experiment. I prepared 20 coding problems and sent every problem to the same model twice. The first version was intentionally short. The second explained almost everything I could reasonably explain without giving away the solution. There were 40 generated solutions in total. No follow-up messages, no asking the model to fix a failing test, no manually repairing imports. The first answer was the answer.
Junie – новый AI-агент от JetBrains. Junior разработчики больше не нужны
В новом переводе от команды Spring АйО мы расскажем вам о новом продукте от компании JetBrains, который называется Junie. Новый продукт работает по принципу искусственного интеллекта и способен самостоятельно выполняет задачи по написанию кода. Компания JetBrains анонсировала Junie, новый агент кодирования, базирующийся на ИИ, доступный через закрытый предпросмотр. По словам компании, Junie способен выполнять поручаемые ему задачи по написанию кода и использовать знания о контексте вашего приложения, доступные через IDE.
