How to Validate a Value Proposition Using LLMs

AI is no longer just a tool for writing emails or generating images—it can act as a 24/7 strategic sparring partner for your business.

For many entrepreneurs in Eastern Europe and Central Asia, testing a new value proposition in the real world is expensive and time-consuming. You need campaigns, feedback loops, and often a budget you may not yet have.
This guide shows you how to use Large Language Models (LLMs) like ChatGPT, Gemini, or Claude to stress-test your idea, uncover blind spots, and refine your messaging—before you spend a single euro.

Define your baseline value proposition

Before involving AI, get clear on what you are testing. Vague inputs lead to vague outputs.
Write your value proposition using this simple structure:
We help [Target Audience] achieve [Desired Outcome] by solving [Specific Problem] with [Your Product/Service].
For instance, a Ukrainian SaaS founder might write: “We help small e-commerce brands in Germany increase repeat purchases by automating personalised email campaigns.”
Keep it simple. Precision at this stage determines the quality of everything that follows.

Program the AI with a target persona

Do not ask the AI if your idea is “good”—that’s too generic to be useful. Instead, turn the AI into your customer.
Give it a clear persona with context: “Act as a busy female e-commerce founder in Serbia managing a team of five. Review this value proposition. What are your immediate thoughts, and would this solve a top-priority problem for you?”
This approach forces the AI to respond with specific, contextual feedback, rather than generic approval.

Run the ‘Devil’s Advocate’ test

Now shift perspective. Instead of validation, actively look for failure points.
Use prompts like: “Act as a sceptical investor. Give me three reasons why this value proposition might fail in the Eastern European market. What objections would a buyer have?”
This step is where most insights emerge. For example, a Moldovan startup might discover that its offer sounds strong—but lacks proof, differentiation, or clarity on ROI. These are issues best solved before going to market, not after.

Simulate customer conversations

Next, turn theory into practice. Use the AI to simulate real-world interactions.
“Let’s roleplay. You are my target customer, and I am pitching this service to you. Ask me five tough questions about pricing, competitors, and implementation, one at a time.”
This exercise sharpens your thinking quickly. You will notice gaps—unclear pricing, weak differentiation, or assumptions you cannot defend. Fix them now, not in front of a real client.

Ask for iteration and refinement

Once you’ve exposed weaknesses, use the AI to improve your idea.
“Based on the issues we identified, rewrite my value proposition to make it clearer, more compelling, and better aligned with my audience. Give me three options.”
You’ll often receive variations you hadn’t considered—simpler, sharper, and more aligned with real customer pain points.

LLMs will not replace real market feedback—but they can compress weeks of thinking into a few hours and help you avoid costly early mistakes.
Action step: Open your preferred AI tool today and run the Devil’s Advocate test on your current idea.
You may not like the answers—but that’s exactly the point.