Picture two identical freelancers. Same portfolio, same rates, same quality of work. One lands the client. The other gets ghosted. The difference? How their AI wrote the proposal.
That was the exact situation a UX designer was dealing with, and it was costing her real business. The author behind this fix, u/badu_111 on r/PromptEngineering, broke down exactly what she changed to turn things around.
Her AI-generated proposals were technically fine. The problem: they all opened the same way. “I am writing to express my interest in…” Every single time. The AI was defaulting to the most statistically average version of “professional proposal” it had ever seen. It looked like everyone else’s output because it basically was.
The Problem With Default AI Mode
When you give AI a basic prompt without structure or constraints, it fills the gaps with what it “knows” a proposal should look like. That means corporate template language, passive voice, and phrases like “passionate about design” and “committed to delivering results.” It’s not wrong exactly, it’s just aggressively average.
Her original prompt was this:
“Write a project proposal for a UX design project for [CLIENT].”
Result: her background front and center, the word “passion” appearing twice, a generic sign-off. The client ghosted. The problem wasn’t price. It was positioning.
The fix wasn’t a longer prompt. It was a smarter structure.
🔧 The Constraint Layer That Changed Everything
She rewrote the prompt with two additions: an explicit structure and a set of hard constraints. Here’s the full version from the post:
“Write a project proposal for a UX design project for [CLIENT] who needs [SPECIFIC PROBLEM].
Structure:
- Open by naming their exact problem in 1 sentence, reference something specific about their product or business
- Show your 3-step process in plain English, no jargon
- Include one past result: [METRIC] for [SIMILAR CLIENT TYPE]
- End with a single yes/no question CTA
Constraints:
- Never start with ‘I am writing to…’ or ‘I would like to…’
- No ‘passion’, ‘dedicated’, ‘committed’ or similar filler words
- No passive voice
- Max 250 words
- Tone: sounds like a consultant who already solved this problem before, not someone applying for a job”
First pass was usable. She sent it same day. The client replied within four hours!
Why This Actually Works
The structure handles the organization. The constraints handle the tone. But the real lever is that last line:
“Sounds like a consultant who already solved this problem before, not someone applying for a job.”
That single frame shifts everything, word choice, confidence level, what the AI decides to include at all. Instead of positioning around qualifications, the output positions around results. Instead of “I would love the opportunity,” you get “here’s what we’re going to do and here’s proof it works.”
As one commenter in the thread noted: the model defaults into “polite applicant mode” unless you explicitly redirect it. The persona frame is what does the redirecting. And it does most of the heavy lifting.
How to Build Your Own Constraint Layer
This approach works beyond proposals, follow-ups, cold outreach, client emails. The framework is the same every time:
- ✅ Define the structure , tell the AI exactly what goes in each section, in what order
- 🚫 Eliminate the defaults , list specific phrases, tones, and patterns to avoid
- ✅ Add a persona frame , not “write a proposal” but “write as a consultant who already solved this exact problem”
- ✅ Set a hard constraint , word count, no passive voice, single CTA, something measurable
The structure and persona frame do most of the lifting. The “what to avoid” list is what stops the AI from sliding back into template mode when it hits an ambiguous section. Without it, the model just fills in the blanks with whatever felt professional during training.
The Bigger Picture
AI defaults to average because average is what it trained on most. The task as a prompter isn’t just to ask for better output, it’s to close off the default paths and redirect toward something specific.
Constraints don’t limit the output. They redirect it. Every banned phrase and hard rule pushes the AI away from the generic center and toward something that actually sounds human, specific, and confident.
Next time your AI output feels flat, skip the rewrite. Add a constraint layer instead. Tell it what it cannot do, and watch what happens to everything it still can.
The full breakdown, plus the library of 50 constraint-based prompts the post’s author is building for proposals, follow-ups, and cold outreach, is over in the original r/PromptEngineering thread. Worth reading if client-facing writing is part of your workflow.
Frequently Asked Questions
Q: What exactly is “polite applicant mode” and why does my AI writing default to it?
Here’s the thing , LLMs default to sounding super deferential because that’s all over their training data. They assume they’re applying for something. To break out of it, you gotta reframe the whole thing. Instead of “I’m writing to express interest,” say “You’re a consultant who’s solved this exact problem before.” Sounds small, but it changes everything , how you structure your answer, the words you pick, how confident you sound.
Q: Can I use constraint layers for types of writing besides proposals?
Yeah, for sure. The article mentions 50 different prompts across proposals, follow-ups, cold outreach, and content. The pattern works whenever you’re stuck in generic mode. Figure out what mode your AI defaults to, then just tell it the tone you actually want instead.
Q: How many constraints do I actually need?
Honestly? Start with one. The commenter nailed it , “sounds like a consultant who already solved this” carried most of the weight. Add the structural rules (no passive voice, no filler) only if the first draft isn’t landing. One good tone frame usually beats five weak instructions.
Q: What’s the difference between writing better instructions and using constraint layers?
Instructions say WHAT to write. Constraints say HOW to think about it while writing it. Good instructions are “write me a proposal.” Constraints are “write me a proposal, but you’re the expert, not the person asking for the job.” That mindset shift is why it actually works.
How a UX designer stopped losing clients to “cheaper” competitors using one prompt structure change
by u/badu_111 in PromptEngineering