The barrier to entry for software development has officially collapsed. You no longer need to spend months learning React, Firebase, or complex backend routing to build a functional, production-ready SaaS application.
Welcome to the era of “Vibe Coding.”
In a recent demonstration, a developer showcased how to build a complete, AI-powered tax management application from scratch in just a few hours. The app allows users to drag and drop their bank statements, uses AI to automatically categorize expenses as business or personal, and calculates their total tax liability.
The most impressive part? The developer didn’t write a single line of code. Instead, he orchestrated a team of AI agents to do the heavy lifting. Here is the exact blueprint for how you can replicate this workflow to build your own complex applications.
The Architecture of Vibe Coding
Vibe coding isn’t just mindlessly throwing prompts at ChatGPT and hoping for the best. Without a strict structural framework, an LLM will hallucinate, lose context, and generate a fragmented, buggy codebase. The secret to this workflow is planning before execution.
To pull this off, the developer used a specific stack of AI tools:
- Google Stitch (for UI/UX): Used to rapidly generate the visual concepts and page layouts.
- Visual PRD (for Technical Scaffolding): A critical tool that analyzes the UI mockups, determines the necessary tech stack (Next.js, Firebase), and generates a comprehensive Project Requirements Document (PRD) containing over 400 step-by-step, verifiable prompts.
- Claude Code (for Execution): A CLI-based AI assistant running locally in the IDE (like Cursor) to execute the prompts, build the file structure, and write the code.
- Claude Web/Desktop (for Auditing): Used as an external “senior developer” to review the code generated by Claude Code, troubleshoot complex bugs, and refine the architecture.
Step 1: The Visual PRD Strategy
The fatal flaw of novice AI developers is asking the AI to build the entire app in one prompt. This inevitably exceeds the model’s context window, leading to disastrous results.
Instead, the workflow begins in Visual PRD. The developer uploads the screenshots generated by Google Stitch. Visual PRD analyzes the images and outputs a highly granular, step-by-step roadmap. For this tax app, it recognized the need for Firebase Authentication, Cloud Firestore (for the database), and Google Cloud Tasks (to handle the heavy background processing of parsing PDFs).
Visual PRD then generates hundreds of sequential prompts. Crucially, each build prompt is paired with a Verification Prompt. After the AI agent writes the code for a specific feature, you feed it the verification prompt, forcing the AI to audit its own work for security flaws, missing dependencies, or logical errors before moving to the next step.
Step 2: The MCP Server Automation
Manually copying and pasting 400 prompts into your IDE is tedious. To solve this, the developer utilized a Model Context Protocol (MCP) Server.
By connecting the Visual PRD project directly to Claude Code running locally in the IDE, the MCP server automates the workflow. You simply tell Claude Code to “start building,” and it will automatically pull the sequential prompts from Visual PRD, write the code, verify the implementation, and move to the next step, effectively building the foundational scaffolding of the app while you watch.
Step 3: Tactical Refactoring and the “Senior Dev” Agent
Once the initial 400-step build is complete, you will have a functional backend, but the front-end UI will likely be fragmented. In this demonstration, the AI generated 90 separate pages when the architecture only required three core views with dynamic slide-out panels.
This is where the external auditing agent comes in.
- Generate a Codebase Map: Ask Claude Code (in your IDE) to generate a full map of the current routing structure.
- The External Audit: Paste that map, along with your original Google Stitch UI designs, into a separate instance of Claude (via the web or desktop app).
- The Fix Prompt: Ask the external Claude agent to write a surgical prompt to refactor the UI, consolidating the unnecessary pages into dynamic panels.
- Execution: Paste that fix prompt back into your local Claude Code instance to execute the changes.
By separating the “thinker” (the external agent) from the “doer” (the local IDE agent), you maintain tight control over the app’s architecture and preserve context windows.
Step 4: Handling Complex Backend Logic (Cloud Run)
For an app that parses 200-page PDF bank statements, standard serverless functions (which time out after a few minutes) will fail. The developer used the external auditing agent to identify this architectural flaw.
The agent recommended switching from standard API routes to Google Cloud Run, which allows background jobs to process for up to an hour. The developer simply asked the auditing agent to write a migration plan, copied the resulting prompt, and pasted it into Claude Code to execute the infrastructure upgrade.
Step 5: The Debugging Loop
When errors inevitably occur (e.g., a “missing index” error in Firebase or a UI component failing to load), the debugging process is entirely agentic:
- Inspect the Console: Grab the exact error log from the browser console or the terminal.
- Provide Context: Take a screenshot of the broken UI.
- Consult the Auditor: Paste the error log and the screenshot into your external Claude agent and ask for a fix.
- Apply the Patch: Feed the solution back into Claude Code.
Vibe coding is not about writing syntax; it is about system design, prompt engineering, and architectural management. By acting as the product manager for a team of specialized AI agents, you can build, refactor, and deploy complex SaaS platforms at a speed that was impossible just a year ago.
