🚀 Generative AI - From Basics to Advanced

A comprehensive repository for learning Generative AI, Large Language Models (LLMs), Prompt Engineering, AI Application Development, and Interview Preparation.

This repository documents my journey of learning Generative AI through practical projects, hands-on coding, and real-world implementations. It will continuously grow with new concepts, examples, notes, interview questions, and production-ready AI applications.

⭐ If you find this repository useful, consider giving it a star!

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📚 What You'll Find

• 📖 Beginner to Advanced Generative AI Notes
• 🤖 OpenAI API Examples
• 💎 Google Gemini API Examples
• 🦙 Local LLMs (Ollama)
• 🔗 LangChain Tutorials
• 🧠 RAG (Retrieval Augmented Generation)
• 📄 PDF Chat Applications
• 💬 AI Chatbots
• 📝 Prompt Engineering
• 🗂️ Vector Databases (Qdrant, ChromaDB, FAISS)
• 🔍 Embeddings
• 🛠️ AI Mini Projects
• ⚡ Streamlit AI Applications
• 🎯 Interview Questions & Answers
• 📈 Best Practices
• 🧪 Experimentation & Research Notes

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🛣️ Learning Roadmap

Python
    │
    ▼
Prompt Engineering
    │
    ▼
LLMs Fundamentals
    │
    ▼
OpenAI API
Gemini API
    │
    ▼
Embeddings
    │
    ▼
Vector Databases
    │
    ▼
RAG
    │
    ▼
LangChain
    │
    ▼
AI Agents
    │
    ▼
MCP
    │
    ▼
Production AI Applications

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📂 Repository Structure

Generative-AI/
│
├── OpenAI/
├── Gemini/
├── Ollama/
├── LangChain/
├── Prompt-Engineering/
├── RAG/
├── Vector-Databases/
├── Embeddings/
├── Streamlit-Projects/
├── Mini-Projects/
├── Interview-Preparation/
├── Notes/
└── Resources/

The structure will evolve as more topics and projects are added.

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🛠️ Tech Stack

• Python
• OpenAI API
• Google Gemini API
• Ollama
• LangChain
• Streamlit
• FastAPI
• Flask
• Vector Databases
• Hugging Face
• Git & GitHub

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📚 Learning Resources

OpenAI

• The official Python library for the OpenAI API

  • https://github.com/openai/openai-python

• OpenAI API Documentation

  • https://developers.openai.com/api/docs

• Chat Completions API

  • https://developers.openai.com/api/reference/resources/chat/subresources/completions/methods/create

• Responses API

  • https://developers.openai.com/api/reference/resources/responses

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Google Gemini

• Gemini API Documentation

  • https://ai.google.dev/gemini-api/docs

• Google Gen AI Python SDK

  • https://github.com/googleapis/python-genai

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🚀 Projects (Coming Soon)

Some projects that will be added to this repository:

• AI Resume Analyzer
• AI PDF Chat
• AI Document Summarizer
• AI Code Reviewer
• AI Interview Assistant
• AI Research Assistant
• AI Email Generator
• AI Content Generator
• AI Notes Generator
• AI SQL Assistant
• AI Image Understanding
• AI Voice Assistant
• AI Chat Application
• Local LLM Applications using Ollama
• RAG Applications
• AI Agent Projects
• MCP-based Applications

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🎯 Interview Preparation

This repository will also include:

• LLM Interview Questions
• Prompt Engineering Interview Questions
• LangChain Interview Questions
• RAG Interview Questions
• OpenAI API Interview Questions
• Gemini API Interview Questions
• AI Agent Interview Questions
• MCP Interview Questions
• Python Interview Questions
• Scenario-Based Questions
• Coding Challenges

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📌 Goals

• Learn Generative AI from scratch.
• Build real-world AI applications.
• Understand production-ready AI systems.
• Master OpenAI and Gemini APIs.
• Learn RAG and AI Agents.
• Explore Local LLMs with Ollama.
• Prepare for AI/LLM interviews.
• Share knowledge with the developer community.

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🤝 Contributions

Contributions, suggestions, and improvements are always welcome.

If you'd like to contribute:

1. Fork this repository.
2. Create a new branch.
3. Make your changes.
4. Submit a Pull Request.

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⭐ Support

If this repository helps you learn Generative AI, please consider giving it a ⭐ on GitHub.

It motivates me to keep adding more high-quality content and real-world projects.

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👨‍💻 Author

Kirankumar J. Meshram

Software Engineer | MERN Stack Developer | AI Enthusiast | Generative AI Learner

Learning in public. Building in public. Growing every day.

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📄 License

This project is licensed under the MIT License.