Course plan: AI-Driven E-Commerce Development with JavaScript and Node.js [draft]

[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.

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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

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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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