How to Prompt: The Anti-Prompt Guide
"Invert, always invert." — Carl Jacobi
Mastering the art of prompting often comes down to understanding exactly what makes a prompt fail. By learning how to write the worst possible prompt, you can guarantee better results by doing the exact opposite.
This guide uses the mental model of Inversion: instead of asking "How do I write a great prompt?", we ask "How do I guarantee the model gives me garbage?"
The Anti-Patterns (How to Fail)
Here is a systematic list of the most reliable ways to sabotage a prompt, grouped by the "Anti-Pattern" and its correction.
1. The "Lazy Delegator" (Vagueness)
The Mistake: Be as vague as possible. Use 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."
✅ Correction (Inversion):
"Write a LinkedIn post about B2B marketing trends in 2025, focusing on AI adoption. Use a professional but provocative tone."
Related principles: The Principle of Explicit Intent — define goal, audience, and success criteria.
2. The "Kitchen Sink" (Overloading)
The Mistake: Ask for everything at once. Mix multiple distinct tasks (explain, code, write a poem) in a single massive block of text. 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. Also, translate the explanation to Spanish."
✅ Correction (Inversion):
Chain your prompts. Break the task into steps.
- "First, explain quantum physics."
- "Now, translate that explanation to Spanish."
Related principles: The Principle of Task Atomicity — keep a single intent per prompt and chain steps.
3. The "Mind Reader" (Missing Context)
The Mistake: Assume the AI knows who you are, who the audience is, and what the goal is. Never assign a role. 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."
✅ Correction (Inversion):
Assign a role and audience. "Act as a senior mechanical engineer. Explain how a car engine works to a 5-year-old using analogies involving toys."
Related principles: The Principle of Explicit Intent — declare role and audience so the agent's output matches expectations.
4. The "Don't Think of a Blue Elephant" (Negative Bias)
The Mistake: Rely 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. Don't use passive voice."
✅ Correction (Inversion):
State what you DO want. "Write in a witty, conversational tone. Use short, punchy sentences. Use active voice."
Related principles: The Principle of Instruction Polarity — prefer positive, prescriptive instructions over negative constraints.
5. The "Chaos Agent" (Structure & Format)
The Mistake: Let the model choose the format. Give contradictory instructions. Provide zero examples (zero-shot). Why it fails: You get whatever format is statistically most common (usually paragraphs). Contradictions lead to confused output.
❌ Anti-Prompt:
"Write a short story but make it extremely detailed. Put it in a table if you want."
✅ Correction (Inversion):
Force the format and use examples. "Write a story in exactly 3 sentences. Output the result as a JSON object with keys 'title' and 'story'. Here is an example..."
Related principles: The Principle of Format Enforcement and Examples — require strict, machine-readable formats and include examples.
6. The "Chatty Cathy" (Fluff)
The Mistake: Treat the AI like a human colleague with small talk. Use weak phrases like "if you can" or "maybe". Why it fails: It wastes tokens, dilutes the signal, and signals low commitment, leading the model to treat instructions as optional.
❌ Anti-Prompt:
"Hi there! I was wondering if you could maybe help me write some code, if it's not too much trouble..."
✅ Correction (Inversion):
Be direct and authoritative. "You are an expert Python developer. Write a script to..."
Related principles: The Principle of Concise, High-Signal Prompts — keep prompts short, remove fluff, and place critical instructions prominently.
The "Anti-Prompt" Checklist
Before sending a prompt, ask yourself: "If I wanted the LLM to fail, would I do this?"
| If you see this... | Do this instead... |
|---|---|
| Vague instructions ("Make it better") | Define success ("Optimize for clarity and brevity") |
| Wall of text | Break into steps or chain prompts |
| No persona | Assign a role ("You are a Senior Editor") |
| "Don't do X" | "Do Y" (Positive constraints) |
| "If you can..." | "You MUST..." (Absolute commands) |
| No format specified | Force format (JSON, Markdown table) |
| No examples | Provide 2-3 examples (Few-shot prompting) |
By avoiding these failure modes, you automatically steer the model toward high-quality, reliable outputs.