About Generative AI
Generative AI is an MCP server published by genieincodebottle in the AI category: explore clean, cutting-edge Generative-AI code—innovate, learn, and build the future of intelligent applications. It has been installed 0 times through Conduid.
The repository has 1.8K stars and 437 forks, with the last commit 6 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.
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README
🎯 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.
260+ modules • 20 tracks • 9 real-world projects • 11 tracks free to start
📚 Table of Contents
📖 Documentation & Learning Resources
🎯 Getting Started
- GenAI Roadmap - Your complete learning path for GenAI
- AI/ML Roadmap - Comprehensive AI/ML learning guide
- AI-ML Companion - Interactive AI/ML learning platform with 17 tracks, 250+ modules, visualizations, quizzes, and hands-on coding (ML fundamentals → LLMs → MLOps)
- 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
- AI Scenario based Interview Q&A - ML/GenAI/Agentic AI Scenario based Interview Q&A
- 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
🛠️ 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
🔗 Quick Access Links
| Category | Resources |
|---|---|
| Learning Platform | AI-ML Companion — Interactive AI/ML learning with 17 tracks, 250+ modules, quizzes & coding |
| 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 |
🤝 Contributing
Contributions are welcome. To add useful resources or code:
-
Fork this repo
-
Clone it
git clone https://github.com/genieincodebottle/generative-ai.git -
Create a branch
git checkout -b feature-name -
Make changes and commit
git commit -m "Your message" -
Push your branch
git push origin feature-name -
Open a Pull Request with a brief description of your changes.
README mirrored from the source repository 2 months ago. The original is authoritative.