How I'd Actually Build a GenAI Product Today
Is your brilliant GenAI demo destined for the graveyard? 30% of them will be abandoned. The reason is simple: a cool demo is not a business. Stop building demos and start building product!
The generative AI gold rush is on.
Every day, we see demos that feel like pure magic, and the temptation to jump in is immense. But I've also seen the other side: the graveyard of brilliant AI prototypes that never became viable products.
They got stuck in the chasm between a cool demo and a real business—a journey so treacherous that Gartner predicts a staggering 30% of GenAI projects will be abandoned by 2025, primarily because they fail to make this critical leap from prototype to a properly governed, production-ready system.
So, how do you cross that chasm?
This isn't another generic list of tools. This is my personal, step-by-step guide, forged from experience, on how I would approach building, launching, and scaling a generative AI application right now.
Section 1: Scoping the Unseen - Finding Your North Star
Before you touch a single line of code, this is where most AI startups either set themselves up for success or failure. The magic of GenAI can be a siren's song, luring you toward solutions in search of a problem.
Finding Product-Market Fit (PMF) in the AI Era
The core question isn't "What can I build with AI?" but "What painful problem can I solve uniquely with AI?" This applies whether you're a B2C founder trying to build the next viral app or a B2B entrepreneur targeting a specific enterprise workflow.
Here’s how I’d validate the idea:
1. Manual-First MVP (The "Wizard of Oz" Method)
Before building a complex AI backend, can you simulate the experience manually? For a B2B tool that promises to summarize legal documents, that means you, the founder, literally read the docs and write the summaries. Charge for it. If you can't get customers to pay for the outcome when you're doing it by hand, they won't pay for it when an AI is doing it. This works for both B2C (e.g., personalized travel itineraries) and enterprise (e.g., custom sales reports). It's the ultimate test of demand, stripped of all tech hype.
2. The Landing Page Test
Put up a simple, clear landing page that explains the value proposition. Not "AI-powered scheduling," but "Never schedule a meeting yourself again." Drive a small amount of ad traffic to it. Do people sign up for the waitlist? This is a classic for a reason - it measures intent before you've invested thousands in development.
3. Customer Development Interviews
Go talk to your target users. But don't ask, "Would you use this?" Ask about their current workflows. For an enterprise client, that's "Walk me through how you generate your quarterly compliance reports." For a consumer, "Show me how you plan your weekly meals." Listen for the pain, the frustration, the "I wish I could just..." moments. That's where your opportunity lies.
4. The Friends and Family Test
Pitch the idea to your friends and family. When they inevitably say it's a great idea, ask for an investment. Even if you don't need the money, this is a crucial test of their true conviction. Think of it as seeking a "soft commitment" like you would in a private equity deal, but you need their investment upfront. Their willingness to put skin in the game is the ultimate validation of how much they truly believe in your idea, moving beyond polite encouragement to tangible support.
The Business Model & The "Quitting Point"
You don't need to make money on day one, but you absolutely need a hypothesis for how you will make money. Is it usage-based? A classic SaaS subscription? A freemium model? The variable costs of AI mean a "figure it out later" approach to monetization is a ticking time bomb.
More importantly, you need to define your "quitting point." This isn't just about running out of money. The most soul-crushing moment for a founder isn't an empty bank account; it's an empty notebook. The hardest time is not when you run out of money, but when you run out of ideas to pivot to. You have to know when the experiment has failed, so you can live to fight another day.
Section 2: The Creation Cycle - From Prototype to Production
Okay, you have a validated idea. Now, how do you build? The most effective founders I know don't choose one path; they master a two-step cycle.
Step 1: "Vibecoding" the Initial Prototype
This is my happy place for getting from zero to one. It's a fluid, improvisational dance with an AI code assistant. You're not just giving commands; you're brainstorming in code.
My go-to tool here is Cursor, and for a few key reasons:
Effortless Setup: It's a fork of VS Code, so the transition is frictionless. You can import all your existing settings, themes, and extensions without a fight. There's no learning curve; you just get superpowers from day one.
Blazing Speed: It just feels faster. This might be my bias, but the token-per-second output, especially with LLMs from providers like Anthropic, is noticeably quicker than competitors like Copilot. This keeps you in a state of creative flow.
Unmatched Context & "Memory": This is its killer feature. Cursor excels at understanding your codebase. You can set rules at both global and project levels (.cursor/rules) that act as a persistent "memory," feeding crucial system prompts and context to the LLM with every query.
Developer-Obsessed: The team behind Cursor is incredibly responsive. They actively engage with developer feedback and ship meaningful improvements at a pace that larger companies can't match. You feel like you're using a tool that's evolving with the community.
For the UI, I'm a huge fan of tools like lovable. It allows you to generate beautiful, production-ready UI components with simple prompts. Its real power lies in a few key areas:
Quality: The quality of the generated code and the UI design is simply amazing.
Flexibility: It beautifully blends AI generation with human editing, allowing you to "vibe" out an interface and then make precise manual adjustments.
Surgical Changes: You can tweak a specific part of the UI without having to rewrite everything, which limits the blast radius for incremental changes.
The magic of this approach is how these tools work in concert: you get the 'feel' of the product right with incredible speed, yet you never sacrifice the granular control that real engineering demands.
Step 2: Architecting the "Last Mile" for Production
The danger of pure vibecoding is creating a "demo doll" - it looks great but falls apart under pressure. This is what I call the "last mile problem" of GenAI. Your prototype gets you 80% of the way to a cool demo, but that final 20% - turning it into a robust, scalable, and secure system - takes 80% of the total engineering effort.
It's here, in this last mile, that you move beyond the "happy path" of a demo and build for the messy reality of production. This is where you put on your architect hat and refactor the promising parts with proper tests, error handling, security, and observability.
The most effective process isn’t about just completing these steps in a linear fashion; it’s a disciplined cycle:
Vibe -> Validate -> Structure -> RepeatYou use the first step, vibecoding, to find the magic and explore the art of the possible at the speed of thought. But that magic is fragile. True value is created in the second step, when you have the discipline to put on the architect’s hat, turning that fleeting moment of brilliance into a robust, reliable foundation.
Founders who only vibe build beautiful demos that break. Founders who only architect build fortresses no one wants to visit.
The winners will be the ones who master the art of being both the artist and the engineer in the same afternoon.
Section 3: The Art of the Prompt - Your Highest Leverage Point
Before we even talk about which model to use, let's get one thing straight: prompting is everything.
People get obsessed with fine-tuning and fancy model architectures. But I'll tell you right now, most teams could achieve a 50% improvement in their app's performance just by spending a week ruthlessly refining their system prompt.
The "take a deep breath" example aside, I found Chain of Draft to be a really really effective way to improve the prompt speed and cost.
Your system prompt is the "constitution" for your AI. It sets the rules, defines the persona, and outlines the goal. Spending 80% of your initial "AI" effort here, before you even consider fine-tuning, is the highest-leverage activity you can do. It's the difference between hiring a brilliant employee and giving them a vague job description versus a crystal-clear mission.
Section 4: The Model at the Core - Don't Overthink It (At First)
Honestly, most people get paralyzed here, and they don't need to be. My take here is - start with what you're comfortable with and what's good enough for the job. You will know when a different model will make a material difference.
Here's my biased, go-to starter pack:
For general app logic and reasoning: OpenAI's o4-mini. It's fast, cheap, and incredibly capable.
For writing and creative generation: Google's gemini-2.5-flash. It has an enormous context window and is fantastic for summarizing large documents or generating creative text.
For coding and tool use: Anthropic's Claude Sonnet 4. In my experience, it's currently the best-in-class for reliably calling external tools and functions.
For text-to-image generation: The flux.1-schnell model, accessible via inference providers like fal.ai. This model is incredibly fast, making it perfect for real-time applications where you need to generate high-quality images with rapid iteration.
One more thing: I personally would not use an LLM for real-time web search within my app unless there's a clear reason for it. The ROI just isn't there yet. The latency is often high, and the cost can be unpredictable.
Section 5: The Tech Stack - Keep It Simple, Stupid
My philosophy here is to keep the stack as simple as possible for as long as possible. The biggest mistake I see is teams jumping to complex, multi-agent frameworks before they've even figured out the core of their business problem.
You'll know when you need that level of orchestration. You'll feel the pain.
Until then, a simple stack works wonders:
1. Frontend
Next.js on top of React. This isn't a controversial choice; it's a strategic one focused on speed and ecosystem. The React ecosystem is unparalleled, giving you access to pre-built components (e.g., from shadcn/ui) that let you build a polished UI without getting bogged down. More importantly, Next.js, built by Vercel, integrates seamlessly with the backend and deployment solutions I'm recommending. This tight integration gives you a massive velocity advantage. Crucially for GenAI, Next.js has first-class support for streaming UI components, which is essential for rendering token-by-token responses from your LLM and creating that interactive, "live" feeling that users now expect from AI applications.
2. Backend
For maximum velocity, my top picks are integrated platforms like Supabase or Vercel. Supabase gives you a Postgres database (with PGVector for RAG), auth, and serverless functions out-of-the-box. Vercel's functions are a natural fit if you're already in their ecosystem with Next.js. This approach abstracts away infrastructure grunt work so you can move faster.
3. LLM Orchestration
For most simple apps, a direct API call to your chosen model is all you need. But for anything more, especially if you're already using Next.js, the Vercel AI SDK is a game-changer. It provides a simple, unified API to interact with different models and has incredible, first-class support for streaming UI components back to the client. This makes building chat interfaces and other real-time experiences almost trivial. For more complex, multi-step agentic workflows, a framework like LangChain is the industry standard, but I'd only reach for it when the complexity truly demands it.
Don't go multi-agent before you have a single, reliable agent that solves a real problem.
Section 6: The Support System - You Can't Do It Alone
Whether you're starting this as a side hustle or going all-in, you need a support system. Building is lonely.
Find Your Tribe: Join a startup community. Whether it's online like Indie Hackers or a local meetup, you need to be around people who get it. They're the only ones who will understand the rollercoaster you're on.
Get an Accountability Partner: Find another founder and set up a weekly check-in. Just 30 minutes to share your wins, your struggles, and what you commit to doing next week. This single habit can be the difference between drifting and executing.
Form Your Personal "Board of Directors": This isn't a formal board. It's your personal mastermind group—a few trusted peers or mentors you can be brutally honest with. They're not there to tell you what to do, but to ask the hard questions and help you see the things you're missing.
The journey of building something from nothing is a marathon, not a sprint. The tech is the easy part. The persistence, the focus, and the resilience—that's what will determine if your magical demo ever becomes a real, enduring business.
Now, go build something amazing.
What's one piece of advice you'd give to someone starting a GenAI app today? I'd love to hear your thoughts.