Lovable Prompt Creator - AI Prompt Generator

## APPLICATION OVERVIEW
This project is a web application designed for AI engineers to create and manage their own large language models (LLMs) using custom datasets. The application will provide a streamlined interface for users to upload data, configure model settings, and monitor training progress, ultimately simplifying the process of LLM creation and deployment.

## CORE FEATURES
1. **Data Upload**: Users can easily upload datasets in various formats (e.g., CSV, JSON) for model training.
2. **Model Configuration**: A user-friendly interface to set parameters such as model size, training epochs, and learning rates.
3. **Training Dashboard**: Real-time visualization of training progress, including metrics like loss and accuracy.
4. **Model Evaluation**: Tools for testing the LLM against a validation set and generating evaluation reports.
5. **Deployment Options**: Options to export the trained model for use in different applications or integrate directly into existing systems.
6. **User Account Management**: Secure authentication for users to save and manage multiple projects.

## DESIGN SPECIFICATIONS
- **Visual Style**: minimalist - Clean, simple design with plenty of white space, minimal color palette, and focus on typography
- **Color Mode**: Light theme with dark text on light backgrounds
- **Primary Color**: #1978E5 (accent for buttons, links, highlights)
- **Typography**: Use Inter from Google Fonts for headings, Inter for body text and UI elements
- **Border Radius**: 8px (moderately rounded) for buttons, cards, and inputs
- **Layout**: The main layout will feature a top navigation bar, a sidebar for quick access to different sections, and a main content area that dynamically updates based on user selections. The dashboard will utilize cards to display metrics and data visualizations.

## TECHNICAL REQUIREMENTS
- **Framework**: React with TypeScript
- **Styling**: Tailwind CSS
- **UI Components**: shadcn/ui
- **State Management**: Zustand or React Context API (choose based on project complexity)

## IMPLEMENTATION STEPS
1. **Set Up Project**: Initialize a new React project with TypeScript and install necessary dependencies (React, Tailwind CSS, Zustand/shadcn/ui).
2. **Design Layout**: Create a responsive layout with a top navigation bar, sidebar, and main content area using Tailwind CSS for styling.
3. **Build Core Features**:
   - Implement the Data Upload feature with file input and validation.
   - Develop Model Configuration forms with controlled components for user input.
   - Create a Dashboard component to visualize training metrics using charts.
4. **Integrate Backend**: Set up an API service to handle data uploads, model training requests, and evaluations.
5. **User Authentication**: Implement user account management with secure authentication and session handling.
6. **Testing and Validation**: Conduct thorough testing of all features and ensure a smooth user experience.

## USER EXPERIENCE
Users will have an intuitive experience starting from the homepage, where they can log in or sign up. Upon logging in, they will access the dashboard where they can upload datasets and configure their models. The training dashboard will provide real-time updates, and users will be able to evaluate their models and export them easily. Clear call-to-action buttons and tooltips will guide users throughout the application, ensuring a seamless interaction flow.