Use AI: Idea to
Deployed Product

Published 21 days ago • 2 min read

We've Been Busy

We're building AI agents for several customers who have signed with us. These projects range from integrating with a 9-figure-revenue company's financial stack, saving them 1,000+ hours weekly on populating invoices and categorizing expenses — to enhancing a sales platform's classification algorithm for better-informed LLM calls across their entire stack.

If your business is interested in understanding how AI can 10x your existing workforce and eliminate the need to hire additional staff, schedule time with us at varickagents.com.

From Idea to Shipped Product

Many of you have brilliant ideas but don't know where to start. You've probably created multiple repositories, spent countless hours in Cursor, and inevitably abandoned projects when you couldn't debug the code.

If this sounds familiar, I have a straightforward solution to help you finally cross the finish line. The key is scoping down your project to a smaller MVP and fleshing out each step using top-tier models before jumping into Cursor.

Step 1: Map Out Your Product

Talk to ChatGPT about what you're building. Use this exact prompt and have a complete back-and-forth conversation until the thread is finished:

"I want you to be my senior software engineering architect and product manager for the product I want to build. Start by asking me for details about what the product should be (ask clarifying questions if ambiguous), who it is meant for, where it is going to live (i.e. iOS/Android App Store or Web Application), and more - to gather as many details as required to map out exactly how to build the product (the technical stack for the frontend, backend, etc), and the order in which features are required (for an MVP, Phase 2, Phase 3, etc). At the end of each message, respond with the % completeness of your understanding of the tech and product stack, and once you are at 100%, give me a COMPLETE, fully fleshed out Product Requirements and Technical Design Document that outlines exactly what to build and how to build it. Ask one question at a time, waiting for my response before continuing to the next step. You decide what belongs in MVP, Phase 1, 2, 3 etc. Optimize for speed and use, not complete feature suites."

At the end of this process, you'll have a complete Product Requirements document from GPT-4o.

Step 2: Create Your Build Specification

Take that document and feed it into ChatGPT o3 or Grok-4 with this exact prompt:

"Based on the document that I've fed you, I want you to build a completely prescriptive document in markdown that breaks down every single task required to build this product, fleshed out with instructions to follow, so that a programming LLM with 0 context can build each feature step by step, with testing in mind, such that the final product is eventually created. Do not skip any steps, and be forward thinking with scalability and robustness in mind. Go for the stack that minimizes costs while also minimizes complexity for a programming LLM to build this successfully with minimum human intervention."

You now have a detailed, line-by-line build specification for an AI agent or yourself to follow.

Step 3: Use the Right Tools

Choose tools that match your needs. Lovable and v0 excel at creating clean frontend UIs but struggle with backend logic. Cursor is powerful for code generation and navigation but assumes coding knowledge.

Here's my go-to stack for shipping quickly:

Frontend: React in Lovable
Backend: n8n (if you can't code) or Python with FastAPI (if you can)
LLM for Coding: Sonnet-4 and Gemini 2.5 Pro
IDE: Claude Code or Cursor
Database: Supabase
Authentication: Supabase Auth
Payments: Stripe
Hosting: Cloudflare

Everything in this stack "just works". LLMs are well-trained on these tools. When starting out, stop trying to save a few dollars at the cost of never shipping anything.