Bring Your Ideas to Life: Vibe Coding for Product Builders

In 1979, James Dyson got frustrated with his vacuum cleaner. The bag kept clogging, suction kept dropping, and no one seemed interested in fixing a solved problem. So he started building. Five years and 5127 prototypes later, he had a bagless vacuum that actually worked. Each failure taught him something the drawings couldn't. "You can't develop new products by asking questions," he later said. "You have to make things, try them, and ask 'does it work?'"

The answer only comes when you build.

As a product builder, you live in the gap between idea and reality. You can describe your idea perfectly in a meeting. You can write detailed specifications. You can sketch wireframes. But until you see your idea running on a screen, responding to clicks, displaying real data - you don't truly know what you've got. And neither does anyone you're trying to convince.

This is why every product builder needs to learn vibe coding.

Why every product builder needs to vibe code

Vibe coding - using AI to build working software through natural language - is the most effective method to bring your product ideas to life. Seeing your idea makes it real. All the gaps in your thinking and imagination get exposed the moment pixels hit the screen. That button you thought was obvious? Confusing. That workflow you imagined as simple? Three steps too many. This feedback loop lets you improve your idea or throw it away before anyone writes production code.

It's also the best method to share your ideas with others. Documents get misinterpreted. Slides get skimmed. Wireframes leave too much to imagination. But a working prototype? Everyone understands a working prototype. You can hand someone your phone and say "try it." No translation required, no details lost to someone else's flawed imagination.

Vibe coding is a forcing function for better specifications. Your prototype reflects the completeness and clarity of your thinking. Vague requirements produce broken prototypes. Contradictory logic produces apps that don't make sense. This brutal feedback makes you a better product thinker - and leads to much better products when engineers build the real thing.

And honestly? Vibe coding is fun. You became a product builder because you enjoy building products. Vibe coding frees you from dependencies on other people or resources. You can just build things. That idea that's been bouncing around your head for months? You can have a working version by dinner.

Solution: Build prototypes through AI-guided development

Here's a quick walkthrough of how it works:

Vibe coding follows three phases: Specify, Build, and Iterate. Let's walk through each.

Phase 1: Specify

Garbage in, garbage out - every engineer knows this. The clarity of your thinking, expressed through a specification document, determines the quality of your prototype.

Use specs you're familiar with. I've spent years writing Product Requirements Documents with user stories and acceptance criteria. It's the format that lets me express my thinking most clearly. But it's just one approach. There are three common ways to provide input:

Spec-driven prototyping. Your words are the primary input method. Write clear requirements, user stories, and acceptance criteria. This is the default approach, accessible to everyone regardless of technical background.

Design-based prototyping. Visual artifacts become your foundation - Figma files, wireframes, even hand drawings on paper. If you're a designer who thinks in sketches, this method lets you communicate with maximum clarity.

Data-driven prototyping. Those with technical backgrounds can leverage existing skills to describe data structures, entity relationships, or data flows. Database schemas and API contracts can drive the AI's understanding.

Combine all three methods for the best results. The more high-quality input you provide, the better your output.

I built an AI skill that conducts a workshop with you to create a detailed Product Requirements Document. It extracts your idea through conversation until it's crystal clear, then structures it in a format AI coding agents understand perfectly. All you have to do is talk to it. If a human engineer can build from your PRD, so can an AI engineer. Check out my dedicated post on creating PRDs with AI.

Phase 2: Build

AI does the heavy lifting, but you can't lean back just yet. Your job is to choose the agent harness, provide the necessary tools, access, and context, and give clear instructions so the AI understands the task.

Choosing your agent harness

The harness for an agent is like a kitchen for a chef. It provides a recipe book (the system prompt), the stove, knives, and pans (tools), ingredients laid out properly (context and configuration), and a ticket system for orders (the interface). Put simply: the harness is what turns a brain into a worker.

A wide range of harnesses exists today. Depending on your technical experience, you can choose between:

Web-based products like Lovable or Bolt - lowest barrier to entry, great for getting started
Full development environments like Cursor or Google Antigravity - more power, more complexity
Terminal-based harnesses like Claude Code or OpenAI Codex - maximum control for those comfortable with command lines

Each harness has individual strengths and weaknesses. There's only one way to find what matches your requirements and skill level: test them all. You won't learn vibe coding by reading about it - you have to practice.

If you're comfortable launching a terminal, I recommend Claude Code. Based on my experience with various agent harnesses, it's currently the most powerful option available.

Providing tools, access, and context

Depending on what you're building, you need to provide the necessary tools, access credentials, and additional context to the AI agent.

Tools. Giving the AI agent additional capabilities through the Model Context Protocol (MCP) is often useful. I usually have the Context7 MCP connected so the agent can access the latest documentation for frameworks, libraries, and SDKs. The Google Chrome Dev Tools MCP is invaluable for building user interfaces - it lets the agent test the result and iterate until it works. Hundreds of MCP servers exist. Choose wisely without polluting your context window with unnecessary tools.

Access. If your prototype integrates with third-party services, provide the necessary credentials. API tokens are common. For locally executed prototypes, a .env file you prepare beforehand is usually all you need.

Context. Sometimes additional context helps dramatically. When building prototypes for work, I keep a context file with our design system basics available. That way my prototype uses the correct colors, fonts, and component patterns. The more useful context you provide, the more likely the agent gets things right the first time.

Crafting your instructions

Put it all together in a concise, precise prompt. Point the AI agent to the specification containing what to build. Introduce the tools it should use. Mention where to find access credentials and the additional context you provided.

Depending on your agent harness, start by sending your initial prompt in planning mode. Claude Code, Lovable, Antigravity, and many others introduced this mode with an optimized system prompt for creating detailed implementation plans. Some agents ask clarifying questions to involve you in key decisions. Review the plan, adjust as needed, and off you go.

Phase 3: Iterate

Depending on task complexity, the AI agent will return after a few minutes with the first result. This isn't the end of vibe coding - it's the beginning. Congratulations, you've made it to the starting line. Now the real work begins.

If you used an IDE or terminal for the first time, you'll probably struggle to run your new application. Just ask the AI agent for instructions, or have it write a README with detailed deployment steps. Most agents will run the app for you if you ask nicely.

Now you'll see the idea that was trapped in your mind for the first time. It's one of the greatest experiences a product builder can have - you've created something from nothing. You probably won't be fully satisfied with the result, which means you'll need to iterate until you are. There's no right or wrong approach. Start somewhere, one change at a time. If your initial result is too far off, revisit your specs.

Pro tip: Run multiple different agent harnesses in parallel and compare their output. Continue with the one you like most, discard the rest.

One final tip: use git to create regular checkpoints you can return to. I sometimes go in directions with my prototypes that I later regret. Instead of trying to undo those changes, just load an earlier commit and pick up from there.

Tips, tricks, and best practices

Start minimal, then expand. Write a minimal spec containing the core idea yourself. Then use my PRD workshop skill to have the agent interview you and flesh out all the details.
Use AI to review AI. Ask a separate AI agent to review your prototype and compare it against your initial spec. Hand those findings to your coding agent to implement fixes.
Leverage existing designs. There's a Figma MCP server that lets you build from existing Figma design files. Super helpful if your company already uses Figma, especially when you want prototypes to look like the real product.
Combine IDE and terminal. IDEs usually have built-in terminals. This lets you use a terminal-based agent harness while having access to all project files at the same time.
Avoid synced folders. Don't build prototypes in folders synced with iCloud, Dropbox, Google Drive, or any file-sharing service. Technologies like NodeJS load dependencies through package managers, and your folder can easily contain thousands of files. File-syncing services hash these one by one before uploading, which will massively slow down your computer. I learned this the hard way.