[100% Off] Generative Ai Real World Projects In Python
GenAI real world projects in Python : Build 3 end-2-end LLM apps with LangChain, RAG, Vector DB, ChatGPT, Google Gemini
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.
Author(s): Unknown
