Course Overview: AI-Driven E-Commerce Development with MERN Stack and Self-Hosted LLMs

#ai #machinelearning #javascript #node

[This is a draft plan, titles can be changed while actually making the course]

Course Overview

Objective:

Develop practical skills to create an AI-enhanced e-commerce platform, focusing on image-based product search, LLM-powered customer support, knowledge retrieval, intelligent recommendations, and multi-lingual functionality.

Structure:

Nine modules with hands-on projects and theoretical insights, culminating in a comprehensive final project.

Syllabus

Module 1: Environment Setup & Foundations

Tools & Setup
- Install Node.js, initialize projects, essential packages
- Set up JavaScript-based LLM tools
- Initialize Git repository
Fundamentals
- Environment isolation
- Version control best practices

Module 2: Image-Based Product Search & Captioning

Image Captioning Pipeline
- Integrate image captioning models
- Generate and store image captions
Vector Database Management
- Convert captions to embeddings
- Store and perform similarity searches
End-to-End Visual Search
- Image upload, caption generation, and search integration

Module 3: Basic Prompt Engineering & Conversational Foundations

Prompt Engineering
- Design and experiment with various prompt types
Conversational API
- Develop API for handling and storing conversations

Module 4: Advanced Customer Support Bot

E-Commerce API Integration
- Connect to an API for order and stock management
Return & Refund Q&A with RAG
- Build a knowledge base and implement retrieval-augmented generation
Sentiment Analysis & Escalation
- Implement sentiment detection and escalation protocols

Module 5: Intelligent Product Recommendations

Similar/Alternative Products
- Embed and retrieve similar items
Personalized Upselling & Cross-Selling
- Generate personalized recommendations based on user history
Dynamic Bundles & Promotions
- Create and propose dynamic product bundles

Module 6: RAG-Driven Knowledge Base (Deep Dive)

Comprehensive Documentation
- Embed product docs and FAQs into a vector database
Human-Like Explanations
- Combine retrieval with LLM generation for detailed responses

Module 7: Customer Feedback & Insights

Feedback Collection & Sentiment Analysis
- Collect and analyze user feedback
Aggregating & Summarizing Feedback
- Summarize key insights from collected feedback

Module 8: Conversational Shopping & Multi-Lingual Support

Chat-First Shopping Flow
- Integrate search functionalities within a chat interface
Multi-Language & Localization
- Implement language detection and localized content delivery

Module 9: Final Project Integration

System Integration
- Combine all functionalities into a unified virtual assistant
Demonstration & Future Directions
- Present the final project and explore potential enhancements

Course Details

Prerequisites: Basic knowledge of JavaScript and Node.js, web development concepts. Completion of the free course Machine Learning Foundations for Software Engineers: A Comprehensive Theory-First Approach is required.

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