I was halfway through editing yet another ChatGPT output last week, replacing “delve” for the third time in a single paragraph, when I stopped and thought: there has to be a better way to prevent this upfront.
Turns out, there is. And it comes down to telling the model what NOT to do, rather than piling on more instructions about what you want.
This Reddit user u/PitifulDrink3776 shared a dead-simple framework on r/ChatGPTPromptGenius that flips the usual prompting approach on its head. Instead of writing massive system prompts trying to describe the perfect output, the author uses three targeted constraints that block the model’s worst habits before they start.
🚀 Quick Start
What you’ll learn: Three copy-paste-ready prompt constraints that stop AI from producing generic, half-finished, corporate-sounding output.
What you need: Any LLM chat interface (ChatGPT, Claude, etc.). Works in custom instructions or individual prompts.
Time to implement: About 2 minutes.
🧠 Why This Works
The core insight here is genuinely smart. Most people prompt by addition: “be professional, be detailed, be thorough.” But the original poster found that subtraction works better. Ban specific bad behaviors, and the model is forced to find alternatives that sound more human and deliver more complete results.
Think of it like sculpting. You’re chipping away the garbage instead of trying to build the perfect output from scratch.
📋 The Three Constraints (Step by Step)
Step 1: The “Negative Vocabulary” Constraint
Ban the model’s favorite crutch words. Here’s the exact prompt snippet from the post:
“You are strictly forbidden from using the following words: delve, seamless, robust, tapestry, dynamic, optimize, leverage, testament, symphony. Do not use introductory filler (‘Sure, I can help with that’) or concluding summaries.”
Why it matters: LLMs default to a small set of “safe” words that instantly scream “AI wrote this.” Banning them forces the model to reach for more specific, natural language. The filler ban is equally important, as it cuts the fluff that pads every response and wastes your reading time.
Step 2: The “No-Placeholder” Rule
This one is specifically for code generation, and anyone who’s gotten a “// insert remaining logic here” comment from an AI knows exactly why it exists. The prompt snippet:
“You must output the complete, exhaustive solution. Do not use placeholders, do not skip boilerplate, and do not summarize the logic. Write every line of required code.”
Why it matters: Without this constraint, LLMs love to take shortcuts on the boring parts: error handling, edge cases, boilerplate setup. Those are exactly the parts that break your app in production. This constraint forces complete, runnable output instead of a skeleton you still need to flesh out yourself.
Step 3: The “Tone Anchor”
Instead of vague instructions like “be professional,” give the model a specific persona. The author’s snippet:
“Adopt the tone of a direct, highly-skilled Senior Developer speaking to a peer. Be concise, opinionated, and highly technical.”
Why it matters: “Be professional” means nothing concrete to an LLM, as it just triggers that sterile corporate voice. A specific persona with specific traits (direct, opinionated, peer-level) gives the model a clear target. The contributor notes this works for copy too: just swap the persona to match your use case.
💡 Tips and Practical Notes
- Negative constraints beat positive ones. Telling the model what to avoid is often more effective than describing what you want. The model has fewer ways to misinterpret “don’t do X” compared to “do something like Y.”
- Customize the banned word list. The nine words above are a great starting point, but pay attention to which words keep showing up in your own outputs and add them.
- Stack all three together. These constraints work best as a set. The vocabulary ban handles word choice, the no-placeholder rule handles completeness, and the tone anchor handles voice. Together they cover the three biggest pain points.
- Put them in custom instructions. The original poster mentions that copy-pasting these into every chat got old fast. Drop them into your custom instructions or system prompt once and forget about it.
- The persona trick scales beyond “Senior Developer.” Writing marketing copy? Try “a sharp copywriter at a top DTC brand speaking to a colleague.” Writing docs? Try “a staff engineer writing internal documentation for senior teammates.”
✅ Next Steps
- Copy the three prompt snippets above into your LLM’s custom instructions right now. Takes 30 seconds.
- Run a before/after test: ask the same coding or writing question with and without these constraints. Compare the outputs.
- Build your own banned-word list based on the AI-isms that bug you most.
- Experiment with different persona anchors for different tasks: one for code, one for copy, one for analysis.
The full discussion lives on r/ChatGPTPromptGenius if you want to grab the snippets directly or share your own constraint ideas with the community.
The “Anti-Lazy” Prompting Guide: 3 constraints to force ChatGPT to drop the corporate voice and write usable code/copy.
by u/PitifulDrink3776 in ChatGPTPromptGenius