"To keep AI coding assistants from running amok, developers must learn to write good specs and develop product management skills. No serious developer still expects AI to magically do their work for them. We've settled into a more pragmatic, albeit still slightly uncomfortable, consensus: AI makes a great intern, not a replacement for a senior developer. And yet, if this is true, the corollary is also true: If AI is the intern, that makes you the manager."
"We see this every day in how developers interact with tools like GitHub Copilot, Cursor, or ChatGPT. We toss around vague, half-baked instructions like "make the button blue" or "fix the database connection" and then act surprised when the AI hallucinates a library that has not existed since 2019 or refactors a critical authentication flow into an open security vulnerability. We blame the model. We say it is not smart enough yet."
"But the problem usually is not the model's intelligence. The problem is our lack of clarity. To get value out of these tools, we do not need better prompt engineering tricks. We need better specifications. We need to treat AI interaction less like a magic spell and more like a formal delegation process. We need to be better managers, in other words."
AI coding assistants perform best as junior contributors rather than replacements for senior developers. Developers must adopt product-management skills and craft precise specifications to guide those assistants. Vague, half-baked instructions commonly lead to hallucinations, insecure refactors, and unexpected behavior from models. Blaming model intelligence overlooks the core issue of unclear instructions. Effective interaction requires treating tasks as formal delegations with clear scope, constraints, and context. Massive, monolithic specs often fail because of context window limits and the model's attention budget, so focused, structured specifications improve reliability and safety.
Read at InfoWorld
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