A month of talking to ChatGPT

Most builds fail before they start. People are already thinking about features before they've understood the problem, the context around it, or what outcome they actually want. The moment you're designing a solution, you stop seeing the problem clearly. The biggest favour you can do yourself is to focus on the problem alone and not get distracted by solutions. If there is one thing you take away from this article, that is it. Building a natural product sense takes years of exposure across industries, problems, and failed attempts. AI already has that exposure. Used correctly, it fills that gap, challenging your assumptions, surfacing what you're missing, and holding you to a structure you'd otherwise skip. Step 1: Define the Problem Break the problem down to its most core form. Write it down, then simplify it one step at a time. Use AI to challenge every word of it: What is the simplest way to state this problem? What assumptions am I already making about it? Is this actually the problem, or just a symptom of something deeper? When did this problem first appear, and what caused it? What will go wrong if I don't solve this? You only leave this step when the problem can't be simplified any further. Don't move on until you're fully satisfied. Step 2: Find the Context This step is about discovering who else has this problem and how they're dealing with it. Use AI to find parallels across industries and surface connections you wouldn't reach on your own: Who else has this problem, and where does it show up? What solutions already exist, and are people paying for them? Why aren't those solutions good enough? What are other problems like this, from other industries? What is the fallout if this problem stays unsolved? At the end of this step, you either have proof that the problem is real and unsolved, or you've already found your answer in a parallel from another industry, or in a solution that already exists that you simply didn't know about. The problem you think you have is rarely the one worth solving. Step 3: Clarify the Outcome Define the final outcome you want. Whatever solution you eventually find or build, this step is about what it needs to achieve. You are not thinking about how yet, only what the end state looks like: What does solving this problem actually achieve? What is the first sign that this problem is going away? Is there another way to achieve this outcome without building anything new? How will you know when the problem is fully solved? Is this outcome solving the problem, or just making it more bearable? Is this outcome worth the effort it would take to reach it? You leave this step with a clear end state that maps directly back to the problem you defined. This is what every decision from here will be measured against. Before You Build Most failed products weren't badly built. They were badly defined. The problem was wrong, the context was unexplored, and the outcome was never clearly stated. No amount of good execution fixes that. Go through each step fully. Don't skip ahead. The clarity you build here is what makes everything after it easier. AI can help you move faster through this process, but you have to be the one doing the thinking. Don't blindly trust what AI produces. AI words can look complete and still say nothing. The human has to go through this journey and use AI as an input, not as the answer. That mindset is what separates a build that works from one that doesn't.