Anthropic updated Claude's system prompt to prevent it from cloning existing video games. The change shows something I see constantly in my own work: telling an AI system what you want is only half the job. You also have to be explicit about what it should not do, and you have to be specific enough that it actually understands the boundary you are drawing.
Last week, Anthropic updated Claude Code's system prompt to block it from recreating existing video games. The company let users know it added instructions that said: do build original games and games from your own designs, don't build recreations of specific existing games. The rule makes sense. It is also the kind of problem I run into constantly when I build automation systems for businesses.
Vague instructions always fail
When I started building systems like WhatsApp automation and appointment reminders for practices, I thought the instructions to the AI could be loose. I told the system to send a reminder when an appointment was coming up, and I expected it to know what a reminder was supposed to say and when to send it. That failed every time.
The system needs to know: Is the reminder for a patient who has not confirmed? A patient who confirmed two weeks ago? Is it three days before or the morning of? Does a text say something different than a voice call? Should it include the doctor's name? These are not small details. If you do not specify them, the system fills in the gaps, often wrongly.
Anthropic is running into the same problem at a different scale. Claude can do many things. Telling it not to clone games is useful. But useful only if the system understands what a clone is, when that rule applies, and what counts as original. The company had to be specific because specificity is how AI systems actually learn boundaries.
Why boundaries have to be detailed
The Anthropic update is not just a line in a rulebook. It is a steer: do this kind of work, not that kind. But if the steer is vague, the AI system will miss it or interpret it wrong. I have watched this happen with my own systems. I told an ad reporting system to alert me when something was wrong. I meant when cost per lead jumped or daily spend spiked unexpectedly. The system had no framework for unexpected. It did not know the difference between a normal shift and a real problem until I made it explicit.
That is why I started building alerts that watch for a specific jump in cost per lead or spend, with clear thresholds and patterns the system can actually measure. Once I wrote down the rule precisely, the system worked. Before that, I was just hoping it would understand what I meant.
Building with AI means being precise about what you don't want
The work of telling an AI system what to do is not one job. It is two. You say what you want: original games, automated reminders, alert me when spend jumps. Then you say what you don't want: no clones, no reminder to the wrong patient at the wrong time, no alerts for normal variation. Anthropic is doing both now. When you build systems for your business, you have to do both too.
I notice that the practices and service businesses that get the most out of automation are the ones who think through the boundaries first. A dentist who says I want appointment reminders usually gets something that works half the time. A dentist who says I want reminders to patients who haven't confirmed, sent three days before, and I do not want them to go to patients who already rescheduled, gets something reliable.
The Anthropic update was necessary because people were using Claude to do something the company did not intend. That is not the company's fault. It is what happens when you give a powerful system minimal instructions. Specificity is the price of control.
