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Generative AI Real world Projects in Python
Hands-on Generative AI with Python : Build 3 End-to-End LLM apps with LangChain, RAG, Vector DB, ChatGPT, Google Gemini
Last updated 2/2026
English
What you'll learn
- Build Generative AI real world projects using LLMs (ChatGPT, Gemini, LLaMA) by completing hands-on GenAI projects used in industry.
- Understand the complete GenAI pipeline — prompt design, embeddings, vector databases, RAG (Retrieval-Augmented Generation), inference, and evaluation.
- Develop GenAI apps using Python — work with text, images, APIs, and tools like LangChain, FAISS, and OpenAI/Gemini SDKs.
- build end-to-end pipelines including data ingestion, preprocessing, RAG, tool calling, and production-ready inference.
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Course content
6 sections • 40 lectures • 3h 28m total length
Requirements
- Basics of Python programming is recommended !
Description
Learn Generative AI by solving Generative AI projects.
Build practical LLM applications using LangChain + RAG, work with Vector Databases, and integrate ChatGPT, Gemini & LLaMA in production-style workflows.
By the end of this course, you will have a strong Generative AI project portfolio, real experience working with LLM APIs, RAG systems, and vector databases, and a clear understanding of how modern AI-powered products are built and deployed in real-world environments.
What You Will Build (Generative AI Real-World Projects) :
Project 1 : Cold Email Generator using LLaMA 3.3
Build an AI-powered cold email generator that:
Analyzes job descriptions or business requirements
Extracts relevant skills and context
Automatically generates personalized, high-quality cold emails
This project demonstrates how companies use open-source LLMs like LLaMA for sales automation and outreach.
Project 2 : Text-to-SQL Generator using Google Gemini
Create an intelligent system that:
Converts natural language questions into SQL queries
Works on real database schemas
Enables non-technical users to query databases using plain English
This project reflects real enterprise use cases in data analytics, business intelligence (BI), and AI-driven decision-making systems.
Project 3 : Food Calorie Detector using OpenAI GPT
Develop a multimodal AI pipeline that:
Takes food images as input
Extracts food information using vision models
Retrieves verified nutritional data
Generates structured calorie, protein, fat, and carb insights using GPT
This project showcases end-to-end GenAI workflows, combining computer vision, retrieval-augmented generation (RAG), and LLM reasoning.
What You Will Learn ?
How to Build Generative AI projects in Python
Create LLM apps using LangChain
Prompt engineering techniques for reliable and accurate outputs
Building RAG (Retrieval-Augmented Generation) systems
Working with embeddings and vector databases
Work with ChatGPT, Google Gemini, and LLaMA
Why This Course Is Different ?
100% project-based learning
Real industry-style use cases (not toy examples)
Multiple LLM providers: OpenAI, Google Gemini, LLaMA
Focus on end-to-end GenAI system design
Portfolio-ready projects for interviews
By completing this course, you won’t just understand Generative AI —
you’ll know how to build, apply, and explain GenAI solutions confidently in real-world scenarios.
Enroll now and start building production-ready Generative AI applications.
Who this course is for:
- Beginners stepping into Generative AI through real-world GenAI projects
- Anyone learning GenAI with Python and wanting to build practical AI applications
- Data Analysts / Developers who want to add GenAI & LLM skills to their profile
- Software Engineers interested in building AI-powered products using LLMs
- Professionals transitioning into GenAI roles like GenAI Engineer or AI Developer
- Students and freshers who want hands-on experience with ChatGPT, Gemini, and LLaMA
- AI enthusiasts curious about building RAG systems, and AI assistants