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
Popular guides: What is RAG • Agentic AI • Transformer Architecture • Interview Q&A • Full Roadmap
Your go-to hub for end-to-end GenAI learning. ⭐ Star this repo to stay updated with the latest GenAI resources :)
- 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 guide · PDF
- Essential GenAI Terms - Illustrated glossary of key terminology · PDF
- What is Generative AI - Full guide to GenAI foundations
- LLM Fundamentals - How transformers and LLMs work · PDF
- What is RAG - Retrieval-Augmented Generation explained end to end
- Vector Embeddings Guide - 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)
- 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
- GenAI on AWS - AWS implementation | GitHub | YouTube
- GenAI on Azure - Azure implementation guide
- GenAI on VertexAI - Google Cloud Vertex AI guide
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.
- 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)
- GenAI Enterprise Production Checklist - Production readiness guide
- 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 - 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
- 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 - Compare OpenAI, Gemini, Claude, Groq + local models (Ollama, HuggingFace)
- Embedding Models - Guide to vector embeddings with Google, OpenAI, and HuggingFace
- 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 - 16+ techniques from basics to APE (Automatic Prompt Engineer)
- Prompt Guard - Detect prompt injections and jailbreak attempts using Meta's Llama Guard
- 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
- n8n Automation - Setup and usage guide for n8n workflow automation platform
| Category | Resources |
|---|---|
| Learning Platform | AI-ML Companion — Interactive AI/ML learning with 22 tracks, 300+ modules, quizzes & coding |
| Learning Path | GenAI Roadmap • AI/ML Roadmap |
| Fundamentals | What is RAG • LLM Fundamentals • Glossary |
| Cloud Platforms | AWS • Azure • VertexAI |
| Interview Prep | Interview Q&A track (13 modules) • GenAI • Agentic AI • LLM |
| Popular Projects | Advanced RAG • Agentic AI • Text-to-SQL |
Contributions are welcome. To add useful resources or code:
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Fork this repo
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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.