[GET] Generative AI Real world Projects: Build End-to-End LLM Apps

[shantanusingh73766]

https://www.udemy.com/course/generative-ai-real-world-projects-build-end-to-end-llm-apps/?couponCode=JAN_2026_SALES

Generative AI Real world Projects: Build End-to-End LLM Apps

Learn Generative AI by building GenAI projects using Python, RAG, vector databases, ChatGPT, Gemini, and LLaMA

Rating: 5.0 out of 5.0
Total Students : 161
Created by Shan Singh
Last updated 1/2026
English [Auto]
4 hours on-demand video


Description :
This Generative AI project based course is designed to help you build real-world LLM applications end to end, using industry-leading models like LLaMA, Google Gemini, and OpenAI GPT..

Instead of focusing on theory alone, this course follows a hands-on, practical approach, where you will design, develop, and deploy 3 production-style GenAI projects that mirror real industry use cases.


What You Will Build (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 Generative AI and LLMs work in real applications
Prompt engineering techniques for reliable and accurate outputs
Building RAG (Retrieval-Augmented Generation) systems
Working with embeddings and vector databases
Using LLaMA, Google Gemini, and OpenAI GPT APIs
Designing scalable and modular GenAI pipelines
Handling hallucinations, cost optimization, and production challenges
Best practices for deploying GenAI solutions responsibly

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.