Why Your AI Outputs Are Mediocre (And How to Fix Them)
Most people treat AI like a person who knows what they mean. It does not. I spent months getting garbage back from what I thought were solid prompts before I figured out the actual mechanism. Picky Kitty Instructions is just a name for a behavior pattern: you write prompts so specific, so over-detailed, that the AI has almost no room to misinterpret you. You are not being difficult. You are removing ambiguity. The core principle is simple. The most common way to get bad output is by assuming the AI will read your mind. You type "write a marketing email" and expect something good. You get generic fluff because the AI is guessing at audience, tone, length, CTA, and brand voice simultaneously. Here is what that looks like when you do it properly: Tell the AI the audience. Tell it the product. Tell it the exact length. Tell it the tone. Tell it what to avoid. Tell it the format. Do not leave any of those blank and expect a good result.Example of weak input: Write me a product description for my new headphones. Example of Picky Kitty Instructions:
Write a 150-word product description for wireless noise-cancelling headphones aimed at remote workers. Tone: professional but warm. Avoid technical jargon. Include one sentence about battery life (20 hours) and one about comfort for long calls. Format as three short paragraphs. End with a single line CTA: "Shop now." Do not use words like "cutting-edge" or "revolutionary."
The second version takes more time to write. The output it generates is closer to what you actually need, usually within one iteration instead of five or six.How to Actually Write Picky Kitty Instructions
I broke this down into a repeatable structure after burning through hundreds of failed prompts. Here is the framework I actually use now: Define the role first. Tell the AI what it is pretending to be. "You are a senior copywriter with 10 years of experience in B2B SaaS." This alone shifts the output quality noticeably. Define the output. Word count, format, structure, tone. Be specific about each one. If you want a table, say so. If you want bullet points, say how many. Define the constraints. What must be included. What must be excluded. This second part is where most people fail. You need to explicitly state what you do not want, not just what you do. Define the failure modes. Tell the AI what a bad answer looks like. "Do not start with 'In today's fast-paced world' or any variation of it." This prevents the most annoying cliché patterns. I have a specific case where this mattered a lot. I was working on a project that required the AI to generate SQL queries based on natural language descriptions. Every time I used a vague prompt, it hallucinated column names that did not exist in the actual database schema. I tried asking it to "be accurate" — which is meaningless to an LLM. The workaround was to include the full schema in the prompt and then add a strict rule: "If a requested column does not appear in the provided schema, output ERROR instead of inventing one." That single constraint eliminated about 90 percent of the hallucination problem. It cost me extra prompt tokens but saved me hours of debugging nonsense queries.There is a practical limitation most people do not consider: long prompts slow you down on every iteration. If you change one detail, you often have to rewrite the whole instruction block. I keep mine in a text file and copy-paste the template, only modifying the variables each time. This cut my average prompt turnaround from about 10 minutes of back-and-forth down to 2 or 3.
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