Want to instantly level up your prompting skills? Stop trying to write "good" prompts. Instead, learn how to write the worst possible prompts—and then do the exact opposite.

The mental model of Inversion is a powerful tool in engineering and problem-solving. As the mathematician Carl Jacobi said, "Invert, always invert." When applied to AI prompting, this means identifying the specific patterns that guarantee failure (hallucinations, vague answers, off-topic ramblings) and ruthlessly eliminating them.

The "Anti-Prompt" Philosophy

We’ve compiled a comprehensive guide based on this philosophy. It identifies the common "Anti-Patterns" that degrade model performance. Here is a breakdown of why they fail:

1. The Lazy Delegator (Vagueness)

The Mistake: Using broad verbs and ambiguous words like "cool", "better", or "nice". Why it fails: The model has infinite degrees of freedom and will regress to the mean, giving you the most statistically likely (mediocre) answer.

• Anti-Prompt: "Write something about marketing."
• Fix: Be specific. "Write a LinkedIn post about B2B marketing trends in 2025."

2. The Kitchen Sink (Overloading)

The Mistake: Asking for everything at once—mixing explanation, coding, and creative writing in one block. Why it fails: It confuses the model’s attention mechanism. Instructions buried in the middle often get ignored ("Lost in the Middle" phenomenon).

• Anti-Prompt: "Explain quantum physics, write a poem about cats, and give me 10 business ideas."
• Fix: Chain your prompts. Break the task into distinct steps.

3. The Mind Reader (Missing Context)

The Mistake: Assuming the AI knows who you are, who the audience is, and what the goal is. Why it fails: Without a persona, the model defaults to a generic, bland "helpful assistant". Without an audience, it guesses (often wrongly).

• Anti-Prompt: "Explain how a car engine works."
• Fix: Assign a role. "Act as a senior mechanical engineer explaining to a 5-year-old."

4. The Negative Bias (Negative Constraints)

The Mistake: Relying only on negative constraints ("Don’t do X", "Don’t be Y"). Why it fails: Models are often bad at negatives and may focus on the very thing you told them to avoid.

• Anti-Prompt: "Don’t be boring. Don’t use long sentences."
• Fix: State what you DO want. "Write in a witty, conversational tone with short sentences."

5. The Chaos Agent (Structure & Format)

The Mistake: Letting the model choose the format or giving contradictory instructions. Why it fails: You get whatever format is statistically most common (usually paragraphs).

• Anti-Prompt: "Write a story but make it detailed. Put it in a table if you want."
• Fix: Force the format. "Output the result as a JSON object."

6. The Chatty Cathy (Fluff)

The Mistake: Treating the AI like a human colleague with small talk. Why it fails: It wastes tokens, dilutes the signal, and signals low commitment.

• Anti-Prompt: "Hi there! I was wondering if you could maybe help me..."
• Fix: Be direct. "You are an expert Python developer. Write a script to..."

Why It Works

Understanding these failure modes gives you a checklist for success. Instead of guessing what might work, you can systematically verify that your prompt doesn’t contain the elements that make it fail.

For example, instead of asking "How do I make this better?", you realize that "better" is a vague anti-pattern. You invert it to: "Optimize this text for a 5th-grade reading level."

Read the Full Guide

Ready to master the art of the Anti-Prompt? Check out our detailed documentation:

👉 How to Prompt: The Anti-Prompt Guide

It includes a full breakdown of the 6 major anti-patterns, examples of failure, and the specific corrections you need to apply.