Bottom line: AI proposals sound robotic not because of the model, but because of the prompt structure. Add a constraint layer telling the model what NOT to do, and the output stops sounding like a template.
There’s a prompt framework circulating in r/PromptEngineering right now that’s worth actually stealing. The author, u/badu_111, has been refining it for client proposals across multiple industries. The insight is simple, but most people skip it entirely because they’re too focused on phrasing what they want.
Why AI Proposals Always Sound the Same
The problem isn’t the model. ChatGPT, Claude, Gemini, they all default to the same pattern when you give them a generic instruction.
“Write a proposal for a SaaS client” gets you something professional-looking, vague, and opening with “I am writing to express my interest.” You already know what I mean. You’ve seen it 40 times. Maybe you’ve sent it 40 times without realizing it.
The real issue is that AI models are trained to be helpful and safe. That combination produces output that’s optimized for inoffensiveness, not persuasion. It hedges, generalizes, and reaches for the most statistically average version of “professional.” Which is exactly why it reads like every other proposal in someone’s inbox.
The fix isn’t rewording your prompt. It’s adding a second layer to it.
What the Constraint Layer Actually Does
Most prompts specify what you want. The constraint layer specifies what you don’t want.
No buzzwords. No vague phrasing. Max 250 words. And this one is doing the most work: “sounds like a human wrote it.”
That last constraint forces the model to break its defaults. You’re not just asking for good output. You’re blocking the bad output it would have generated anyway. Think of it like a bouncer at a club. You’re not just inviting the right people in. You’re actively keeping the wrong ones out.
The word limit matters more than most people think. When you cap the output at 250 words, the model has to prioritize. It can’t pad with filler. Every sentence has to justify its space. That constraint alone removes about 60% of the generic noise.
The framework also structures the email itself in a specific way: open with their problem (one sentence), show your process in exactly 3 concrete steps, include one specific past result, and close with a low-friction CTA: “20-minute call” instead of “let me know your thoughts.”
The “exactly 3 steps” constraint is worth sitting with. Not “a few steps.” Not “some steps.” Three. Specificity in the instruction produces specificity in the output. When you’re vague about structure, the model fills that vagueness with whatever pattern is most common in its training data. Which, again, is the template you’re trying to escape.
Every element earns its place. Especially “don’t mention price in the email.” Keep the focus on value first. Cost conversation comes later, in person, when there’s already a reason to care.
Use Cases
This isn’t just for proposals. The constraint-layer approach works anywhere “sounds human” is the bar:
- ✉️ Follow-up emails after a sales call, where “just checking in” is the kiss of death
- Cold outreach that doesn’t read like a mass blast, one specific detail about the recipient breaks the pattern immediately
- LinkedIn DMs that open a real conversation: constraint: no pitch in the first message, no “I’d love to connect”
- Any client-facing copy where generic = ignored, onboarding emails, renewal reminders, upsell sequences
In each case, the move is the same. Write your normal prompt, then add a block that starts with “Constraints:” and lists everything the output is not allowed to do. You’ll be surprised how much junk that single addition cuts out.
Prompt of the Day
Write a proposal email for a [CLIENT TYPE] who needs [SERVICE]. Structure: 1. Open by naming their specific problem in 1 sentence, never start with ‘I am writing to…’ 2. Show your process in exactly 3 steps, concrete not vague. 3. Include one specific past result: [RESULT] for [SIMILAR CLIENT TYPE]. 4. End with a low-friction CTA: a 20-minute call, not ‘let me know your thoughts.’ Constraints: max 250 words, no buzzwords (no ‘synergy’, ‘leverage’, ‘holistic’), tone is professional but sounds like a human wrote it, do not mention price.
Fill in the brackets. Run it against your last proposal and compare them side by side. The gap will be obvious. And once you see it, you won’t be able to unsee it in every piece of AI copy you’ve sent before.
Save it as a template and swap the brackets each time. The constraint block stays exactly the same, that’s the part you never change.
One Layer More
A commenter in the thread made a good point: even a solid constraint prompt gets better when you give the model real context about past client conversations. Things like: what objections came up, what language the client used to describe their own problem, what outcome they said they cared about. Constraints tell the model what to avoid. Context tells it what to aim at. Together they do what neither does alone.
Start with constraints. Then layer in specifics about the actual client. That combination is where proposals go from “pretty good for AI” to something a real person would read twice.
What part of your workflow still produces bad AI output even with a solid prompt? Leave it in the comments and let’s work through it.
Frequently Asked Questions
Q: My constraints are tight, but proposals still sound templated. What am I missing?
Context beats constraints. Based on commenter feedback, the real shift happens when you feed the model actual past client conversations, their specific pain points, and results you’ve achieved. Your 3-step process gives structure, but without real examples, the model defaults to generic language. Try adding 1-2 actual client wins to your prompt as reference material, the model will calibrate its tone and specificity from there.
Q: How do I format past client examples so the model actually uses them?
Keep it scannable. For each win, include: client type, the exact problem they faced, your 3-step process, and the real result (numbers or outcome). Don’t paste email threads, summarize in 2-3 sentences. The model pattern-matches much better when it can quickly see your previous work in a clear, structured format instead of digging through text.
Q: What if I don’t have a portfolio of past client wins yet?
Start with hypothetical clients in your niche and use the framework to write sample proposals, then test them with real prospects. Once you land 2-3 actual results, add them to your prompt as context. The framework scaffolds your thinking; real examples just make it feel less formulaic when prospects read it.
Sharing the prompt framework I use to write client proposals that actually close — works with ChatGPT, Claude, and Gemini
by u/badu_111 in PromptEngineering