Adding more requirements to an AI prompt helps only up to a point. Past that point, every added bullet makes the output worse, not better.
Why it matters: Developers keep piling detail onto prompts to fix bad output, not realizing the pile itself is the problem.
The Details:
- Dense prompts flatten priority. Every requirement lands with equal weight, so the model has no signal for which constraint is load bearing and which is a mild preference. When two items conflict, and in real code they eventually do, the model resolves it silently with no flag or warning, so you get code that satisfies every line yet misses the point.
- Context and constraints are not the same thing wearing the same word. Background about the system, how it deploys, what broke last time, composes cleanly and always helps. A checklist of thirty things to satisfy simultaneously competes for the same attention budget and starts interfering with itself past a handful of items.
- Detail you strip from a prompt does not vanish, it has to live somewhere. Tests state what must be true and get executed, not skimmed. Types enforce constraints instead of merely stating them. Interfaces hold boundaries and project files hold standing conventions, so the prompt only carries what matters for this one turn.
- The practical move is to send a short brief stating goal and hard limits, then spend the next round steering only the part that came back wrong. That means treating each exchange as a conversation rather than a one shot delivery, which trades the illusion of certainty for actual iteration.
Bottom Line: The developers getting the best results are not writing the most detailed prompts, they are learning what to leave out and where to put it instead.
