Generative AI Resources
Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.
What is Generative AI Resources?
Generative AI Resources is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to comprehensive resources on generative ai, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.
Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.
This server falls under the Knowledge & Memory category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Comprehensive resources on Generative AI, including a detail
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx generative-aiConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Generative AI Resources
The Generative AI Resources repository by genieincodebottle is a large open-source learning hub containing over 260 educational modules, 20 structured learning tracks, and 9 real-world projects covering the full spectrum of generative AI development — from foundational LLM concepts through advanced agentic systems, RAG patterns, multi-agent orchestration, and production deployment on AWS, Azure, and Google Vertex AI. Developers, data scientists, and AI engineers use it as a reference library for frameworks like LangChain, LangGraph, CrewAI, and n8n, and as interview preparation material with scenario-based Q&A guides.
Prerequisites
- Git installed for cloning the repository
- Python 3.9+ for running the Jupyter notebooks and project code
- API keys for the LLM providers you want to use (OpenAI, Google Gemini, Anthropic Claude, Groq, etc.)
- Jupyter Notebook or JupyterLab installed to run the interactive notebooks
Clone the repository
Clone the generative-ai repository to your local machine.
git clone https://github.com/genieincodebottle/generative-ai.git
cd generative-aiExplore the repository structure
The repo is organized into tracks. Browse the GenAI Roadmap document to identify where you want to start — foundational concepts, RAG patterns, agentic AI, or a specific framework.
Set up a Python virtual environment
Each project subdirectory has its own requirements. Create a virtual environment before installing dependencies.
python -m venv venv
source venv/bin/activate # macOS/Linux
# or: venv\Scripts\activate # Windows
pip install -r requirements.txtConfigure your API keys
Most projects read API keys from environment variables or a .env file. Set up the keys for the LLM providers used by the project you are working with.
export OPENAI_API_KEY=your-openai-key
export GOOGLE_API_KEY=your-gemini-key
export ANTHROPIC_API_KEY=your-anthropic-key
export GROQ_API_KEY=your-groq-keyLaunch Jupyter and open a notebook
Start Jupyter Lab or Notebook and navigate to the module you want to study or run.
jupyter labGenerative AI Resources Examples
Client configuration
This repository is a learning resource, not a packaged MCP server. When used as an MCP knowledge source the typical setup runs the notebooks locally.
{
"mcpServers": {
"generative-ai": {
"command": "npx",
"args": ["generative-ai"]
}
}
}Prompts to try
Example questions and tasks aligned to the repository's content areas.
- "Explain the 9 advanced RAG patterns covered in the generative-ai repository"
- "What is the recommended learning path for someone new to agentic AI?"
- "Show me how to implement a multi-agent system using CrewAI and LangGraph"
- "What interview questions does the repo cover for LLM engineering roles?"
- "How do I implement Text-to-SQL using LangChain and PostgreSQL based on the examples here?"Troubleshooting Generative AI Resources
Notebook kernel crashes when importing LangChain or other heavy dependencies
Each module has its own requirements.txt. Make sure you have activated the correct virtual environment and installed dependencies from the specific project subdirectory, not the root.
API calls fail with authentication errors
Verify your API keys are exported in the current shell session or placed in a .env file at the project root. Many notebooks use python-dotenv to load .env automatically — ensure dotenv is installed: pip install python-dotenv.
Neo4j connection errors when running graph Q&A projects
The graph-based projects require a running Neo4j instance. Use the provided Docker command or Neo4j AuraDB free tier. Set NEO4J_URI, NEO4J_USERNAME, and NEO4J_PASSWORD in your environment before running the notebook.
Frequently Asked Questions about Generative AI Resources
What is Generative AI Resources?
Generative AI Resources is a Model Context Protocol (MCP) server that comprehensive resources on generative ai, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Generative AI Resources?
Follow the installation instructions on the Generative AI Resources GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Generative AI Resources?
Generative AI Resources works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Generative AI Resources free to use?
Yes, Generative AI Resources is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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