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generative-ai

Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.

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Repository

README

From the repo.


         

🎯 Learn AI/ML Interactively

I built AI-ML Companion - every AI, ML, GenAI and Agentic AI concept covered here, taught visually with animated diagrams, quizzes, and hands-on Python. The guides below are the full, continuously-updated versions of the reference material in this repo.

300+ modules • 22 tracks • 9 real-world projects • free to start

Try AI-ML Companion

Popular guides: AI History • Cheatsheets • Interview Q&A • Full Roadmap • GenAI Imp Docs • ML Project Interactive Visualization • AI Blogs


Your go-to hub for end-to-end GenAI learning. ⭐ Star this repo to stay updated with the latest GenAI resources :)

📚 Table of Contents

📖 Documentation & Learning Resources

🎯 Getting Started

  • AI-ML Companion - Interactive AI/ML learning platform with 22 tracks, 300+ modules, visualizations, quizzes, and hands-on coding (ML fundamentals → LLMs → MLOps)
  • GenAI Roadmap - Your complete learning path for GenAI (interactive)  ·  markdown
  • AI/ML Roadmap - Comprehensive AI/ML learning  ·  PDF

🧠 Core Concepts & s

🏗️ Architecture & Technical Stack

☁️ Cloud Platform Guides

💼 Career & Interview Preparation

Interview Q&A track - 13 always-current Q&A modules across ML, GenAI, and Agentic AI (free preview questions, full sets with Pro). The PDFs below are downloadable companions to these live modules.

🚀 Production & Enterprise

🛠️ Practical Use Cases & Projects

🔍 Retrieval-Augmented Generation (RAG)

  • Advanced RAG - Comprehensive RAG techniques including agentic, graph, multimodal, and 9 advanced patterns (corrective RAG, hybrid search, query expansion, etc.)
  • Cache-Augmented Generation - Alternative to RAG using context caching for faster responses

🤖 Agentic AI & Orchestration

💬 Conversational AI

🔧 LLM Providers & Tools

  • LLM Providers - Compare OpenAI, Gemini, Claude, Groq + local models (Ollama, HuggingFace)
  • Embedding Models - Guide to vector embeddings with Google, OpenAI, and HuggingFace

📊 Data & Analytics Applications

🎨 Prompt Engineering & Security

  • Prompt Engineering - 16+ techniques from basics to APE (Automatic Prompt Engineer)
  • Prompt Guard - Detect prompt injections and jailbreak attempts using Meta's Llama Guard

🖼️ Multimodal & Specialized

⚡ Automation

  • n8n Automation - Setup and usage guide for n8n workflow automation platform

🔗 Quick Access Links

CategoryResources
Learning PlatformAI-ML Companion — Interactive AI/ML learning with 22 tracks, 300+ modules, quizzes & coding
Learning PathGenAI Roadmap • AI/ML Roadmap
Cloud PlatformsAWS • Azure • VertexAI
Interview PrepInterview Q&A track (13 modules) • GenAI • Agentic AI • LLM
Popular ProjectsAdvanced RAG • Agentic AI • Text-to-SQL

🤝 Contributing

Contributions are welcome. To add useful resources or code:

  1. Fork this repo

  2. Clone it

    git clone https://github.com/genieincodebottle/generative-ai.git

  3. Create a branch

    git checkout -b feature-name

  4. Make changes and commit

    git commit -m "Your message"

  5. Push your branch

    git push origin feature-name

  6. Open a Pull Request with a brief description of your changes.

Collected info

  • ★ 2,529 stars
  • ⎇ 613 forks
  • Language: Jupyter Notebook
  • Source updated: 6/29/2026

Config for your environment

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Config file: ~/.cursor/mcp.json

{
  "mcpServers": {
    "mcp-server": {
      "url": "{MCP_ENDPOINT_URL}"
    }
  }
}

Paste into mcpServers in the config file. Restart Cursor after saving.

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