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

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.

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 (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.