🏴‍☠️ Blaming ChatGPT? Try This Prompt

Add Exclusions, See Difference

Author

Captain YAR
January 03, 2026 • Estimated Reading Time: 8 minutes

Most people think ChatGPT is getting dumber, but the hard truth is that it’s usually user error. I recently came across a breakdown by an AI expert that completely reframes how we should talk to these models. It turns out, we’ve been missing a few critical structural elements in our daily workflow.

The original poster argues that the frustration comes from vague instructions. Instead of treating the AI like a magic 8-ball, this industry pro suggests treating it like a new employee who needs a very specific job description. The difference between a mediocre output and a gold-standard response often boils down to a systematic framework.

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The Engineering Behind the Prompt

The core of this method relies on structured engineering. The author breaks it down into a specific template that forces the AI to “think” before it speaks. It’s not just about asking a question: it’s about setting boundaries and logic constraints. The framework specifically combines Role, Task, Context, Reasoning, Format, and a Stop Condition. By explicitly defining the “Reasoning” step and setting a “Stop Condition,” the creator of this method ensures the model doesn’t hallucinate or ramble endlessly.

The Power of the Stop Condition

Many of us forget to tell the AI when to finish the job. The expert points out that adding a “Stop Condition” is crucial for quality control. It acts as a final checklist for the model. Instead of just saying “write 5 tips,” you tell the AI “The task is complete when 5 strategies are provided, validated for accuracy, and clearly actionable.” This forces the model to self-evaluate against your criteria before delivering the final output.

Defining Boundaries with Exclusions

The LinkedIn user emphasizes that telling the AI what not to do is just as important as telling it what to do. In the provided example regarding career coaching, the prompt explicitly excludes generic advice like “dress well.” This technique forces the model to dig deeper into its training data for less obvious, higher-value answers. If you don’t build these walls, the AI will always take the path of least resistance.

Enforcing Logic Checks

I really appreciate this addition from the post’s author: the “Reasoning” step. The template asks the AI to “Apply clear reasoning” based on validation or logic checks. This connects to Chain-of-Thought prompting principles. By explicitly asking the model to base its recommendations on data or validated practices, you move the output from creative fiction to grounded analysis. It turns the chatbot into a research assistant that has to show its work!

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Try This Template

Here is the exact template shared by this innovator that you can copy and paste:

Act as [Role] to [Task].
Consider the following context: [Context – details, rules, and exclusions].
Apply clear reasoning: [Reasoning – validation, accuracy, logic checks].
Return the response in this format: [Output format].
The task is complete when [Stop condition].

The Trade-off

While this framework is powerful, the original poster honestly notes that it’s not a magic fix for everything. Writing these structured prompts takes significantly longer than a quick one-liner. There is also a risk that being too rigid with your constraints might limit the AI’s “creativity” or lateral thinking capabilities. It requires a bit of practice to balance strict instruction with enough freedom for the AI to surprise you.

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