The Experiment
A few months ago, I set out to build something specific: an AI Visibility Audit tool that could analyse a brand’s presence across AI-generated search results and surface actionable recommendations. Not a toy. Not a prototype to show at a dinner party. A real tool that could deliver real value.
The catch — I was not going to write a single line of code.
I am not a developer. I can read code, I can talk to developers, and I understand software architecture well enough to be dangerous in a product meeting. But I have never shipped production code myself. What I wanted to test was whether AI platforms had reached the point where someone with domain expertise but no coding ability could build functional software.
The answer turned out to be yes — but with a long list of caveats that are far more interesting than the headline.
The Process: Three AI Platforms, One Tool
I did not use a single AI to build this. I used three — Claude, ChatGPT, and Gemini — sequentially, each for different stages of the build. Not because I planned it that way from the start, but because I learned quickly that each platform has distinct strengths and weaknesses.
Claude was my architect. When I needed to think through the logic of the tool — what inputs it would take, what analysis it would perform, how the output should be structured — Claude was the best thinking partner. It asked clarifying questions. It pushed back on vague requirements. It helped me build a spec that was coherent enough to actually implement.
ChatGPT was my builder. When it came time to generate the actual code — the scripts, the API integrations, the data processing logic — ChatGPT was faster and more willing to produce complete, runnable code blocks. It was less likely to hedge and more likely to give me something I could test immediately.
Gemini was my researcher. When I needed to understand specific APIs, find documentation, or validate technical approaches, Gemini’s integration with Google’s knowledge base made it the most reliable source for up-to-date technical information.
This multi-platform approach was not elegant. It was messy, iterative, and occasionally frustrating. But it worked — and it taught me something important about how AI tools actually function in practice.
The Intern Analogy
The best description I can give for working with AI on a build like this is that it is like managing a team of brilliant but wildly inconsistent interns.
Each one is capable of extraordinary work. They can produce in minutes what would take a junior developer days. They understand context, they can reason through problems, and they occasionally surprise you with insights you had not considered.
But they also hallucinate. They forget what you told them ten minutes ago. They confidently produce code that looks perfect but fails silently. They sometimes solve the wrong problem with impressive thoroughness. And they require constant, specific, structured direction to stay on track.
This is the gap between the AI hype and the AI reality. The technology is genuinely powerful. But the idea that you can hand it a vague brief and get back a finished product is fantasy. What you actually need is the ability to break a complex problem into small, well-defined tasks, verify each output independently, and course-correct constantly.
In other words, the hard part is not the AI. The hard part is the thinking.
Five Lessons From the Build
After completing the tool — which, to be clear, works and is in active use — I walked away with a set of lessons that I think apply far beyond my specific project.
Lesson 1: Prompting is project management. The quality of what you get from AI is directly proportional to the quality of your instructions. Vague prompts produce vague outputs. Specific, structured prompts with clear constraints and expected formats produce useful results. If you are good at writing project briefs, you will be good at prompting AI. It is the same skill.
Lesson 2: AI cannot replace domain expertise. It amplifies it. I could build this tool because I understood SEO, AI visibility, and what a useful audit looks like. The AI handled the implementation. But every decision about what to build, what data to analyse, and what recommendations to generate came from years of professional experience. A developer with no marketing knowledge could not have built this tool even with the same AI platforms, because they would not have known what to ask for.
Lesson 3: Verification is non-negotiable. Every single output from every AI platform needed to be tested. Not spot-checked — tested. Code that looked correct had subtle bugs. Data processing logic that seemed right produced wrong results on edge cases. I estimate I spent as much time verifying and debugging AI-generated output as I did generating it. If you skip this step, you will ship broken software and not know it until a user tells you.
Lesson 4: Context windows are a real constraint. Every AI platform has a limit on how much context it can hold in a single conversation. For a complex build, you will hit that limit repeatedly. When you do, the AI loses track of earlier decisions, contradicts itself, and produces code that does not integrate with what it generated an hour ago. I learned to keep a running document of all decisions, specs, and code — outside the AI — and feed relevant sections back in as needed. Think of it as the AI’s external memory.
Lesson 5: The multi-platform approach is underrated. Using different AI platforms for different tasks was not just a workaround — it was genuinely more effective. Each model has different training, different strengths, and different failure modes. Leaning into those differences rather than forcing one platform to do everything produced better results across the board.
What This Means for Non-Technical Operators
The real takeaway from this experiment is not that AI can replace developers. It cannot — at least not yet, and not for anything complex. What it can do is dramatically expand the range of what non-technical people can build.
If you are a marketer, a strategist, a founder, or an operator with deep domain knowledge and no coding ability, you are now capable of building functional tools that would have required a developer six months ago. That is a genuine shift in leverage.
But it only works if you bring structured thinking to the process. The AI is the engine. You are the driver. And if you do not know where you are going, the engine’s horsepower is irrelevant.
My advice to anyone considering a no-code AI build: start small, pick a problem you understand deeply, use multiple AI platforms, verify everything, and document relentlessly. The technology is ready. The question is whether you are ready to think clearly enough to direct it.
