The Curse of Knowledge: Why Your Smartest People Write the Worst AI Prompts

The Expert's Biggest Blind Spot

Here is a weird truth we see every single week: The more someone knows about a subject, the worse they are at talking to AI about it. It sounds backwards, right? You would think a veteran accountant would write the best tax bot, or a master coder would write the best documentation prompt. But they usually don't. In fact, they often struggle the most.

We call this the 'Curse of Knowledge.' It happens because experts forget what it is like to not know something. They skip the basics. They use shortcuts. They assume the AI 'just gets it.' But AI does not have intuition. It has patterns and data. When you leave gaps in your instructions, the AI fills those gaps with hallucinations. This is why so many founders feel like their AI is 'stupid' when, in reality, the instructions are just incomplete.

Why Jargon is Killing Your ROI

In our experience, experts love big words. They use professional shorthand that has taken them twenty years to learn. When they prompt an LLM, they throw these terms around and expect the model to understand the specific nuance of their specific business. Here is the thing: An AI might know the dictionary definition of your jargon, but it does not know how *your* company uses it.

The Assumption Trap

When an expert writes a prompt, they often miss three critical things:

• Context: They forget to explain the 'Who, What, and Why' of the task.
• Constraints: They don't tell the AI what NOT to do.
• Step-by-Step Logic: They jump straight to the finish line without showing the work.

Most prompts fail because they are treated like a wish. In engineering, we treat them like a blueprint. A wish is vague; a blueprint is exact.

Consultants vs. Engineers: The Prompting Gap

We see a common pattern in the market. Many consultants will tell you that prompting is an 'art form.' They will sell you expensive workshops on how to talk to the machine. They overcomplicate the process with fancy names and long-winded strategies. Let's be honest: that is a waste of your time. You don't need an artist; you need an engineer.

At Ezibell Tech, we believe prompting is an engineering discipline. It is about building a repeatable system. While a consultant focuses on the 'vibe' of the response, an engineer focuses on the reliability of the output. We don't just 'write prompts.' We build architectures that ensure the AI behaves the same way every single time, whether it is through Python-based frameworks or structured system prompts in a mobile app.

From 'Chatting' to Engineering Systems

If you want to move past the experimental phase, you have to stop 'chatting' with your AI. You need to start building. This means moving away from a single, long prompt written by a frustrated expert. Instead, we break tasks down into smaller, manageable chunks. We use modern engineering practices like version control for prompts and automated testing for outputs.

The Power of the System Prompt

In our work with Flutter and React Native apps, we see founders trying to bake logic into the UI. That is a mistake. The real magic happens in the system prompt—the hidden layer of instructions that tells the AI exactly how to behave before the user even types a word. Experts often ignore this layer because they are too busy tweaking the individual questions. But the system layer is where the stability lives.

Stop Guessing and Start Shipping

The 'Curse of Knowledge' will keep your team spinning in circles if you let it. You can keep asking your experts to 'try one more time' to get the prompt right. You can keep tweaking words and hoping for a better result. Or, you can treat your AI implementation like a serious piece of software engineering. This is the difference between a project that looks cool in a demo and a product that actually generates revenue.

We have seen many teams struggle with the transition from 'it works on my machine' to 'it works for a thousand customers.' The bridge between those two points is not more jargon—it is better engineering. You can spend months debugging your team's assumptions internally, or you can bring in a team that has deployed these types of architectures multiple times this year. If you're ready to stop experimenting and start shipping real production-grade AI, let's look at your architecture.