# How I Built a Review Site with 800+ Articles Using AI

> Source: <https://dev.to/_1a008d053e73e4a54d13a/how-i-built-a-review-site-with-800-articles-using-ai-5fle>
> Published: 2026-05-23 04:35:47+00:00

A few months ago, I wanted to build a review site for Chinese consumer brands — products like GaN chargers, USB-C hubs, smart home devices, and laptops that are popular in Asia but don't get much coverage in English-language tech blogs.
The goal was simple: produce useful, data-driven reviews at scale. No clickbait, no affiliate-first garbage. Just honest comparisons with real specs and real user feedback.
Here's how I built it, what the workflow looks like, and what I learned along the way.
The site runs on a minimal stack:
No backend to manage. Every article is a Markdown file in the repo. Decap CMS gives the content team a nice UI on top, but the source of truth is Git.
I didn't want to build yet another AI-generated content mill. The approach was different:
The AI handles the heavy lifting — research, formatting, translation of Chinese reviews — while humans control the quality bar.
Here's the actual workflow for each article:
The articles that perform best aren't the ones with keyword-stuffed titles. They're the ones with actual benchmarks and real user experiences. A USB-C cable buying guide with measured charging speeds and compatibility testing gets more engagement than any generic listicle.
Publishing 3-5 articles daily (focused, well-researched ones) built organic traffic faster than trying to write one perfect article per week. Search engines reward freshness.
Chinese e-commerce platforms have incredibly detailed review systems, often with photos. Translating and aggregating authentic user feedback gives articles depth that pure spec sheets can't match.
Early tests with full AI generation produced articles that looked good but lacked depth. They'd say "great product" without explaining why. The fix was adding real user quotes and verified test data.
We tried building an automated image pipeline using pattern-matched CDN URLs. It failed constantly. The solution was going back to sourcing images manually from official brand sites.
The first batch of articles tried too hard to match search patterns. They read like SEO sludge. The fix was writing for humans first and treating keywords as a secondary concern.
Not explosive growth, but steady, sustainable progress.
If you're building something similar or have questions about the workflow, drop a comment below. Happy to share more details about specific parts of the pipeline.