# Vibe Coding is Broken. This Fixes It!

Source: https://www.youtube.com/watch?v=CP6nRNTIWC4
Recap page: https://rapidrecap.app/video/CP6nRNTIWC4
Generated: 2025-11-19T14:05:23.171+00:00

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## Quick Overview

The video demonstrates that Emergent's Agent platform fixes the perceived broken state of "vibe coding" by providing a cohesive, multi-component architecture that handles frontend hosting, authentication via Google OAuth, backend API logic (using FastAPl/Motor/async MongoDB), RAG implementation via Google File Search API, and comprehensive testing/deployment, culminating in a successfully tested and deployed RAG-as-a-Service platform.

**Key Points:**
- The custom RAG-as-a-Service platform development utilizes an architecture including Edge Hosting/CDN for the frontend (React/Next.js/Vue/Svelte), Authentication (Clerk/Firebase/Auth0/Supabase), Backend API (Node.js/Hono/Python/Go), File Storage (AWS S3/R2/Uploadthing), Payments (Stripe/LemonSqueezy/Paddle), Database (Postgres/MySQL/Marge/Redis), and LLM integration (Google Gemini 2.0 Flash via Emergent LLM Key).
- The agent successfully implemented user authentication using Emergent Google Social Login, session management with 7-day HTTP-only cookies, and user profile handling.
- Knowledge Base Management was implemented, allowing users to create multiple knowledge bases, upload PDF, TXT, CSV, and MD files (max 50MB), and delete knowledge bases with cascade cleanup.
- The RAG-Powered Chat System supports multiple chat sessions per knowledge base, persistent chat history stored in MongoDB, and uses Google Gemini 2.0 Flash for all responses, including file attachments.
- The agent resolved initial issues, including a redirect URL setting error during Google OAuth and a CORS issue related to `allow_credentials=True` when using cookies.
- The final application features a modern, responsive dashboard UI built with Tailwind CSS, and the entire system is designed to be highly scalable, supporting users on its infrastructure.
- The process concludes with the agent providing a comprehensive summary, deployment instructions (costing 50 credits/month per deployment), and successfully testing the core RAG functionality.

![Screenshot at 00:08: Initial architectural diagram showing the core components: Client/Frontend hosted on Edge Hosting/CDN, connected to Authentication, Backend API, File Storage, Payments, and Database.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-00-08.png)

**Context:** The video showcases the development process of a Retrieval-Augmented Generation (RAG) as-a-Service platform using the Emergent AI coding platform. The goal is to create a full-stack web and mobile application that integrates multi-user support, knowledge base management, and AI-powered chat capabilities, addressing the complexity often associated with building such systems from scratch, which the presenter refers to as "vibe coding".

## Detailed Analysis

The video walks through building a complex RAG-as-a-Service platform using the Emergent AI coding agent, which is highlighted as a superior alternative to traditional "vibe coding" due to its integrated, scalable infrastructure. The architecture is clearly laid out, encompassing a Client/Frontend layer (supporting React, Next.js, Vue, Svelte) hosted on Edge Hosting/CDN, an Authentication service (using Clerk, Firebase, etc.), a Backend API supporting various languages (Node.js, Hono, Python, Go), File Storage options, a Payments module, and a Database (MongoDB/Postgres/Redis). The agent specifically plans to use Google File Search API for RAG indexing and Google Gemini 2.0 Flash as the LLM, secured via an Emergent LLM Key. During the build, the agent proactively identified and fixed issues, including an incorrect redirect URL for Google OAuth processing and CORS configuration errors related to session credentials. Upon completion, the agent provided a detailed summary of implemented features, including user authentication, knowledge base management supporting PDF/TXT/CSV/MD uploads, persistent chat history in MongoDB, and a modern dashboard UI built with Tailwind CSS. The final steps involve testing the Google OAuth flow and preparing for deployment, which costs 50 credits/month on Emergent's scalable infrastructure.

### Initial Architecture Overview

- Client/Frontend on Edge Hosting/CDN
- Authentication (Clerk, Firebase, Auth0, Supabase)
- Backend API (Node.js, Hono, Python, Go)
- File Storage (S3, R2, Uploadthing)
- Payments (Stripe, Paddle)
- Database (Postgres, MongoDB, Redis)

### Agent Configuration

- Using Claude 4.5 Sonnet model with Ultra thinking (16k tokens) enabled
- Selecting E-1 (Stable & Thorough) agent type
- Connecting to a Private GitHub Repository

### RAG Implementation Details

- Utilizing Google File Search API for indexing knowledge bases (PDF, TXT, DOCX supported)
- Using Google Gemini 2.0 Flash via Emergent LLM Key for AI responses

### Authentication and Data Persistence

- Implemented Emergent Google Social Login (simpler, managed authentication)
- Secure Google OAuth integration via Emergent Authentication
- Session management with HTTP-only cookies (7-day expiry)
- Persistent chat history stored in MongoDB

### Backend and Frontend Stack

- Backend uses FastAPI + Motor (async MongoDB) + emergentintegrations
- Frontend uses React '18 + React Router + Tailwind CSS + Axios
- API Endpoints include /api/auth/session, /api/auth/time, /api/auth/logout, Knowledge Bases CRUD, and Chat Session management.

### Testing and Resolution

- Agent proactively identified and fixed an issue where the Google OAuth redirect URL was incorrectly set to '/dashboard' instead of '/auth' for session ID processing
- CORS issue related to `allow_credentials=True` was resolved by checking backend settings.

### Final Output and Deployment

- Agent provided a comprehensive summary of features, tested the system, and presented deployment options, noting a cost of 50 credits/month for a production-ready environment.

![Screenshot at 00:08: Initial architectural diagram showing the core components: Client/Frontend hosted on Edge Hosting/CDN, connected to Authentication, Backend API, File Storage, Payments, and Database.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-00-08.png)
![Screenshot at 00:40: A detailed overview of the components required for the RAG platform, including Authentication, Edge Hosting/CDN, Backend API, File Storage, Payments, and Database.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-00-40.png)
![Screenshot at 02:05: The Emergent platform interface where the user selects the agent type \(E-1 Stable & Thorough\) and model \(Claude 4.5 Sonnet\) before initiating the build process.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-02-05.png)
![Screenshot at 02:27: The agent queries the user for clarification on key architectural decisions like API keys, authentication methods, supported file types, and UI design preferences.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-02-27.png)
![Screenshot at 02:56: A visual summary grid highlighting Emergent's features, including Native Backend capabilities \(Auth, Payments, Scaling\), Agentic Vibe Coding, and One-click Deployment.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-02-56.png)
![Screenshot at 04:30: The process of creating a new private GitHub repository named 'file\_search\_emergent' to house the generated code.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-04-30.png)
![Screenshot at 06:47: The agent generates a comprehensive summary report detailing the implemented features across User Authentication, Knowledge Base Management, RAG-Powered Chat System, and Dashboard UI.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-06-47.png)
![Screenshot at 08:15: The deployment modal appears, indicating that starting a new deployment costs 50 credits/month and requires hitting 'Start Deployment'.](https://ss.rapidrecap.app/screens/CP6nRNTIWC4/00-08-15.png)