Most people assume that when ChatGPT gives a generic or hallucinated answer, the model is simply failing to be smart enough for the task at hand. The hard truth is that the model is merely reflecting the quality of the instructions it received, and most of us are essentially whispering vague requests into a hurricane. I recently stumbled upon a comprehensive guide from an AI professional who identified 37 specific ways to fix broken prompts. This list is a wake-up call for anyone who relies on Large Language Models for daily work but feels frustrated by the inconsistency of the output.
📌 The Mechanism: Why Your Prompts Are Probably “Sucking”
The core concept the original poster addresses is that Large Language Models are probabilistic engines, not mind readers. When you provide a short, open-ended prompt, you are forcing the AI to guess the context, tone, format, and goal based on the statistical average of its training data. This inevitably leads to “average” results that look like everything else on the internet. The expert behind this resource argues that “fixing” a prompt is rarely about changing a single word; it is about fundamentally restructuring the architecture of your request. By applying specific repair tactics, like the ones found in this 37-point checklist, you move the AI from a state of creative guessing to a state of precise execution. It is the difference between asking a stranger to “buy food” and giving a professional chef a detailed recipe with a budget and dietary restrictions.
💡 Insight 1: Context is the Anchor for Quality
One of the most critical takeaways from studying the methods shared by this industry pro is that context is not optional. Many of the fixes in the list likely revolve around front-loading the prompt with background information that you might take for granted. If you ask the AI to “write a LinkedIn post about sales,” it has no anchor. It drifts into clichés about closing deals and hustling. However, applying the principles shared by this creator means you must inject specific variables: who the audience is, what the specific product does, the tone of voice required, and the ultimate goal of the post. The fix here is to stop treating the chat box like a search bar. A search bar takes keywords; a reasoning engine requires a briefing. The author’s approach suggests that if you aren’t spending at least thirty seconds typing your prompt, you probably aren’t giving the model enough fuel to reach the destination you have in mind.
💡 Insight 2: The Power of Constraints and Negative Prompting
Another major theme in high-level prompt engineering, which is undoubtedly covered in the expert’s 37 ways, is the art of telling the AI what not to do. This is often just as powerful as telling it what to do. Beginners focus solely on the positive instruction, such as “write a blog post.” Advanced users, like the innovator who compiled this list, know that you must set guardrails. This involves explicit constraints such as “do not use the word ‘delve’,” “keep sentences under 20 words,” or “avoid passive voice entirely.” By artificially narrowing the solution space, you force the model to work harder to find the correct words, often resulting in much sharper, more human-sounding prose. It is about removing the fluff before it is even generated. The fixes provided by this LinkedIn creator likely emphasize that every constraint you add increases the probability of a high-quality output by eliminating the pathways that lead to mediocrity.
💡 Insight 3: Formatting and Role-Based Instruction
The final pillar of fixing bad prompts involves controlling the persona and the presentation. The talented creator who shared this visual guide understands that how the information is presented is often just as important as the information itself. A common “fix” is assigning a specific role, telling the AI to act as a “Senior React Developer” or a “Fortune 500 Copywriter.” This primes the model to access a specific subset of its training data associated with that expertise. Furthermore, requesting specific output formats is a massive efficiency hack. Instead of getting a wall of text, you can ask for a markdown table, a JSON object, or a bulleted executive summary. The 37 ways likely include specific syntax for these requests, turning a messy brain dump into a usable artifact. It transforms the interaction from a conversation into a production line for specific deliverables.
Potential Challenges and Nuances
While having 37 ways to fix a prompt is an incredible resource, there is a risk of over-engineering your workflow if you aren’t careful. You do not need to apply every single fix to every single prompt. The goal is to diagnose why a specific output failed and apply the relevant patch. If the tone is off, use a persona fix; if the facts are wrong, use a context fix. The savvy professional who built this list provides a toolkit, not a rigid rulebook. You must develop the intuition to know which tool to pull from the belt.
This collection of tactics is exactly what is needed to bridge the gap between novice experimentation and professional deployment. If you want to see the full breakdown of all 37 techniques, I highly recommend looking at the original source!
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