generative-ai

🎯 Learn AI/ML Interactively

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

400+ modules • 28 tracks • 23 real-world projects • free to start

Blog - Inside a Production Multi-Agent GenAI System Popular guides: AI History • Cheatsheets • Interview Q&A • Full Roadmap • GenAI Imp Docs • ML Project Interactive Visualization • AI Blogs

📚 Table of Contents

• Documentation & Learning Resources
• Practical Use Cases & Projects

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📖 Documentation & Learning Resources

🎯 Getting Started

• AI-ML Companion - Interactive AI/ML learning platform with 28 tracks, 400+ 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

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

🏗️ 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

Interview Q&A track - 20 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.

• GenAI & Transformers Q&A - GenAI and transformer interview prep  ·  PDF
• RAG, Prompting & Modern GenAI Q&A - Retrieval, prompting and applied GenAI
• LLM Interview Prep - Core LLM questions  ·  Scenario-based
• Agentic AI Interview Prep - Agent-specific interview prep  ·  PDF
• Agentic AI Scenario Q&A - Multi-agent scenarios  ·  90+ Q&A (PDF)
• ML System Design Q&A - System design for ML/AI roles
• Also in the track: Classical ML • Deep Learning • MLOps • ML Math & Stats • ML Coding • Forward Deployed Engineering
• AI Roles & Important Topics - Career paths and topics (PDF)

🚀 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
• Tura - Local, open-source coding agent with CLI, TUI, web, and desktop interfaces for context-aware development workflows
• AI Patterns - 23 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
• agent-qa - Self-improving QA agent for natural-language web/mobile tests with persistent memory, MCP, and Agent Skills
• YYLO - Command-line orchestrator for coding agents with typed task, validation, merge, and release-readiness boundaries in a dedicated branch/worktree
• agent-manager - Terminal UI that runs coding-agent CLIs such as Claude Code, Codex and Gemini CLI side by side, each in its own persistent tmux session, with live status, git worktrees and diff review
• deed.page - Agent skill and remote MCP server that let a coding agent publish an HTML page or small static site to a public URL with one API call

💬 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 Image
• 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 Platform	AI-ML Companion - Interactive AI/ML learning with 28 tracks, 400+ modules, quizzes & coding
Learning Path	GenAI Roadmap • AI/ML Roadmap
Cloud Platforms	AWS • Azure • VertexAI
Interview Prep	Interview Q&A track (20 modules) • GenAI • Agentic AI • LLM
Popular Projects	Advanced 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.