E-Commerce on AI VOID

Guided Project 2: An E-commerce Product Listing

Sat, 25 Oct 2025 02:00:00 +0000

9. Guided Project 2: An E-commerce Product Listing

This project will guide you through building a responsive e-commerce product listing application with Next.js. We’ll focus on displaying a catalog of products, implementing filtering and search capabilities, and creating individual product detail pages. This project will reinforce your understanding of data fetching, dynamic routes, and building interactive UI components in Next.js.

Project Objective: Create an e-commerce-style product listing application where:

Users can view a grid of products.
Users can filter products by category.
Users can search for products by name.
Each product has a dedicated detail page.
The application is responsive and user-friendly.

Technology Stack:

Project 2: Developing a B2B E-commerce Platform Module

Wed, 06 May 2026 00:00:00 +0000

Introduction: Architecting for Enterprise E-commerce

Welcome back, future Angular architects! In our previous project, we laid the groundwork for complex enterprise applications. Now, we’re diving into a crucial domain for many businesses: a B2B E-commerce Platform. This isn’t your typical consumer-facing online store; B2B e-commerce often involves intricate pricing, customer-specific catalogs, order approvals, and robust account management.

In this chapter, we’ll begin building a core module for such a platform: the Product Catalog and Search Module. This will give us a chance to apply advanced Angular concepts like scalable component architecture, efficient data fetching, and intelligent filtering. We’ll leverage modern Angular features, including standalone components, and explore how AI can assist in accelerating our development workflow, from data modeling to component generation.

Chapter 16: Project: Data Extraction for E-commerce Product Listings

Mon, 05 Jan 2026 00:00:00 +0000

Introduction: Turning Product Text into Gold

Welcome back, future data wizard! In our journey so far, you’ve mastered the fundamentals of LangExtract, understood how to set up your LLM provider, and crafted basic extraction schemas. Now, it’s time to put that knowledge to the test with a real-world, highly practical project: extracting structured data from e-commerce product listings.

Imagine you’re building a tool to compare prices across different online stores, or perhaps enriching your own product catalog with information scraped from various sources. The raw data often comes as messy, unstructured text – a product name, a description paragraph, a list of features, all jumbled together. Our goal in this chapter is to transform this chaotic text into clean, structured data like product names, prices, descriptions, and key features, using LangExtract’s powerful LLM-orchestrated capabilities. This project will solidify your understanding of schema design, prompt engineering, and handling common data extraction challenges.