Most people are wasting their time with AI because they prompt like amateurs. They treat powerful language models like simple search engines, typing in vague queries and expecting magic in return. When the result is mediocre, they blame the technology.
But the problem usually isn’t the AI; it’s the input. I recently discovered a breakdown by this LinkedIn creator that perfectly highlights the gap between average users and power users. The difference comes down to control.
The Mechanics of Control
The core philosophy here is shifting from passive asking to active directing. When you leave a prompt open-ended, the AI guesses your intent based on the “average” of its training data. This results in safe, generic, and often boring responses. The expert points out that by adding specific constraints, like role, context, and format, you narrow the model’s focus. You are essentially pruning the decision tree the AI uses to generate text, forcing it to ignore the “average” answers and dig for the specific gold you actually need.
📌 Anchor the AI with a Specific Persona
The first major takeaway from this post is the power of role-playing. The author emphasizes that you shouldn’t just ask a question; you should tell the model who is answering it. If you ask for investment advice, a generic model gives you a disclaimer and a definition. However, by commanding the AI to “Act as a financial advisor,” you effectively unlock a specific subset of its training data. This sets the tone, vocabulary, and expertise level immediately. It creates a simulation where the AI adopts the mindset of the specific expert you need, rather than just being a generic text generator.
💡 Inject Context and Surgical Instructions
Context is king, and this industry pro highlights it perfectly. A prompt like “Explain photosynthesis” yields a standard textbook definition that might be too complex or too simple. But as the post suggests, adding specific background information changes everything. By saying, “I’m a biology teacher explaining to 10th graders,” you dictate the complexity level. Coupled with direct action verbs, this clarity eliminates ambiguity. It is the difference between asking for a summary and demanding the model “summarize this article in three sentences.” The author proves that the more specific the instruction, the less room there is for the AI to drift.
✅ Dictate the Final Format
The final piece of the puzzle is visualization. The creator notes that many users forget they can control exactly how the data looks. You don’t have to accept a wall of text that is hard to read. By specifying “list the pros and cons in a table” or requesting a bulleted list, you force the AI to structure its reasoning logic. This is incredibly useful for comparative analysis or data extraction. Instead of spending twenty minutes formatting the text yourself, you make the AI do the heavy lifting upfront.
The Nuance of Over-Constraint
While these tactics are powerful, there is a balance to strike. If you layer on too many constraints or conflicting roles, the model can sometimes get confused or “hallucinate” to try and please every instruction. It is about finding the “sweet spot” of enough direction without over-engineering the prompt.
The original post includes a full infographic with even more hacks, as the author mentions 30 in total! You should definitely check out the full source link to see the rest.