🏴‍☠️ You’re Treating AI Like Google

The secret to better ChatGPT outputs

Captain YAR
December 12, 2025 • Estimated Reading Time: 8 minutes

ChatGPT isn’t the problem with your output quality, your inputs are.
That might sound harsh, but it’s the reality for most users struggling to get usable results.

I recently stumbled across a brilliant breakdown by this AI professional that completely shifts the perspective on how we communicate with LLMs. The expert argues that bad outputs are almost always a result of user error, not AI limitation.

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The Mechanism of Contextual Framing

The core concept the author explores is the difference between a search query and a delegation instruction. The creator demonstrates that the AI operates exactly like a literal-minded intern; if you give it vague or ambiguous instructions, it fills in the gaps with generic fluff. It’s not about the model’s intelligence being lacking; it’s about the user’s precision being low.

This LinkedIn user emphasizes that effective prompting requires a shift from passive questioning to active direction. You cannot expect the AI to read your mind regarding context, timelines, or tone. You have to explicitly build the frame, or the picture will always come out crooked.

The Specificity Shift

The first major takeaway involves moving from open-ended wondering to data-driven requests. The original poster points out that asking a question like “How did my content do?” is essentially useless because “good” is entirely subjective to the AI. Instead, the expert suggests anchoring the request in specific data points.

The example provided is: “Can you provide a breakdown of LinkedIn content performance for March as compared to January?” This forces the AI to look at relative growth and specific metrics rather than just spitting out a generic summary. By defining the timeframe and the comparison model, the author ensures the output is analytical rather than descriptive.

The Simplicity Paradox

There is a common misconception that complex tasks require complex language, or that “prompt engineering” involves using a thesaurus. This industry pro highlights that trying to sound smart actually degrades performance. Using phrases like “decrypt the quintessence of polycontextual semiotic constructs” is a surefire way to confuse the model or trigger a hallucination.

The innovator suggests stripping away the jargon. A better approach is to ask: “Can you explain the differences in our customer support and customer acquisition process in simple terms?” The lesson here is that clear, simple language yields clear, high-quality thinking.

The Structured Feedback Loop

Perhaps the most valuable trick the person who shared it offers is the structured feedback prompt. Asking “Is this website good?” usually results in a polite, unhelpful “Yes, it looks nice.” The post’s author replaces this with a rigorous framework that demands critical analysis.

The suggested prompt is: “Can you tell me specifically what sections and copy on our website you found most useful, and why? Also, please rate them on a scale of 0 – 10 and justify your rating. If any section or copy has a rating less than 8, suggest 4 improvements for it.” This turns the AI into a critic rather than a cheerleader!

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