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
• Practical Use Cases & Projects

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📖 Documentation & Learning Resources

🎯 Getting Started

• GenAI Roadmap - Your complete learning path for GenAI
• AI/ML Roadmap - Comprehensive AI/ML learning guide
• Essential GenAI Terms - Key terminology and concepts
• LLM Fundamentals - Core concepts of Large Language Models

🧠 Core Concepts & Guides

• Vector Embeddings Guide - Understanding vector representations
• Prompt Engineering - Crafting effective prompts
• AI Patterns - Top 25 AI design patterns
• ML Reference Guide - Machine learning reference

🏗️ Architecture & Technical Stack

• GenAI Tech Stacks - Technology stack overview
• LLM Providers - Comparison of LLM providers
• Advanced RAG Decision Flow - RAG architecture guide
• GenAI Project Lifecycle - End-to-end project guide

☁️ Cloud Platform Guides

• GenAI on AWS - AWS implementation | GitHub | YouTube
• GenAI on Azure - Azure implementation guide
• GenAI on VertexAI - Google Cloud Vertex AI guide

💼 Career & Interview Preparation

• GenAI Interview Q&A - Common interview questions
• Agentic AI Interview Q&A - Agent-specific interview prep
• 90+ Multi-Agentic AI Interview Q&A - Multi-Agentic specific interview prep
• AI Roles & Important Topics - Career paths and topics

🚀 Production & Enterprise

• GenAI Enterprise Production Checklist - Production readiness guide

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🛠️ 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

• Agentic AI - Multi-agent systems with CrewAI & LangGraph frameworks
• AI Patterns - 25 advanced reasoning patterns (Chain-of-Thought, ReAct, Tree-of-Thought, Meta-Prompting, etc.)
• MCP - Model Context Protocol - Standard protocol for LLM tool interoperability with web search
• Multi-Agentic Prod Grade Content Moderation System - AI-Powered Multi-Agentic Content Moderation System with React Frontend
• Handling Latency in Multi-Agentic System - How to handle Latency in Multi-Agentic System

💬 Conversational AI

• Chatbot with Memory - PDF chatbot using local models with persistent conversation memory
• Conversational Analytics - Full-stack app analyzing customer feedback (React + FastAPI + PostgreSQL)

🔧 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

• Text-to-SQL - Convert natural language to SQL queries with visualization
• Graph Q&A - Query Neo4j graph databases using natural language
• Sentiment Analysis - Analyze customer call transcripts for sentiment and aggressiveness
• Your AI Chat Analytics - Chat analytics dashboard

🎨 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

• Gemini Nano Banana - Text-to-image generation with Gemini 2.5 Flash
• Llama 4 Multi-Function App - All-in-one app: chat, OCR, RAG, and agentic AI

⚡ Automation

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

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🔗 Quick Access Links

Category	Resources
Learning Path	GenAI Roadmap • AI/ML Roadmap
Fundamentals	Essential Terms • LLM Fundamentals • Embeddings Guide
Cloud Platforms	AWS • Azure • VertexAI
Interview Prep	GenAI Q&A • Agentic AI Q&A
Popular Projects	Advanced RAG • Agentic AI • Text-to-SQL

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🤝 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.