How to Use AI to Rewrite Your Product Descriptions for Search

A practical guide to learn how to use ai to rewrite your product descriptions for ecommerce and marketing managers who want better visibility without more manual work

Your product descriptions were probably written for humans. That made sense five years ago. Today, they need to work for search engines and AI assistants too.

The good news: AI can help you rewrite them. The better news: you can automate the entire process. And there's an even bigger opportunity most retailers haven't discovered yet.

Let's walk through all three levels.

Level 1: Using AI to Rewrite Product Descriptions

Most product descriptions fall into one of two traps. They're either too generic ("High-quality running shoes for everyday use") or too technical (specs that mean nothing to someone searching "comfortable shoes for standing all day").

AI can bridge this gap by rewriting descriptions to match how people actually search.

Here's a simple approach:

Take your existing product description and feed it to ChatGPT, Claude, or any LLM with a prompt like: "Rewrite this product description to naturally include search terms people would use when looking for this product. Keep it readable and helpful, not keyword-stuffed."

You can make this more effective by adding context. Include the top search queries for your product category (pull these from Google Search Console, Semrush, or even Google's autocomplete suggestions). Ask the AI to weave these naturally into the description.

Before:

"Premium leather wallet with RFID blocking technology. Multiple card slots and bill compartment. Available in black and brown."

After:

"A slim leather wallet that actually fits in your front pocket without the bulk. The RFID blocking keeps your cards safe from digital pickpockets, no sleeve needed. Holds 8 cards plus cash without looking stuffed. If you're tired of sitting on a brick or worrying about contactless theft, this is the upgrade."

The second version answers real searches: "slim wallet for front pocket," "RFID blocking wallet," "wallet that's not too bulky." It reads naturally but captures more intent.

Level 2: Automating the Process

Rewriting one description is easy. Rewriting 500? That's a project no one has time for. And even if you did it once, search trends shift. What people searched for last year isn't what they're searching for now.

This is where automation changes the game.

Tools like n8n, Make, or Zapier let you build workflows that run on a schedule, no coding required. You can set up a system that pulls your product data, sends it through an AI rewrite process, and updates your product pages automatically.

A basic automation flow looks like this:

First, pull your product catalog from your ecommerce platform (Shopify, WooCommerce, BigCommerce, most have APIs or direct integrations).

Second, for each product, fetch current search trend data for that category.

Third, send the product details plus search data to an AI model with your rewrite prompt.

Fourth, push the updated description back to your product page.

Fifth, schedule this to run every two weeks or monthly.

If you're comfortable with a bit of code, you can build this even faster with Python scripts or what some people call "vibe coding", using AI to help you write the automation itself. Ask Claude or ChatGPT to write you an n8n workflow or a Python script that does exactly this. Most of the work is already done for you.

The result: Your product descriptions stay fresh and search-aligned without anyone manually touching them. You set it up once and it keeps running.

This alone puts you ahead of most competitors who are still working from product descriptions written when they first launched.

What This Approach Captures (And What It Misses)

Automated AI rewrites will improve your visibility for generic product searches. Someone searching "slim RFID wallet" is more likely to find you. That's real value.

But there's a category of search this approach doesn't touch: location-based queries.

Think about how people actually shop. They don't just search "running shoes." They search "running shoes near me" or "where to buy Nike Air Max near me" or ask ChatGPT "what stores have hiking boots nearby."

76% of people who search for products locally visit a physical store within a day only if they find what they're searching for. These are high-intent buyers ready to purchase. But a single product page on your ecommerce site can't capture these searches, it's not structured for location-specific queries.

If you have physical stores, you're sitting on an opportunity that optimized product descriptions alone won't unlock.

Level 3: The Location-Based Opportunity

Imagine if every product in your catalog existed as a separate, search-optimized page for each store location. Not just "running shoes" but "running shoes at your Brooklyn store," "running shoes at your Chicago store," and so on.

500 products across 50 locations equals 25,000 pages, each one capturing a specific local search that your main website can't.

This is what the most sophisticated multi-location retailers are building. It's not traditional SEO. It's creating a discovery layer that connects products to places.

We built TNG Shopper so you share your ecommerce URL, we generate thousands of product-location pages automatically. They stay updated. They're structured for Google and AI search. No setup, no dev resources required.

Curious what this would look like for your catalog? Get a visibility analysis.

Where to Start

If you haven't touched your product descriptions in a while, start with Level 1. Pick your top 20 products and run them through an AI rewrite. See what changes. Measure the impact.

If that works, invest a weekend into building the automation. There are tutorials everywhere for connecting your ecommerce platform to AI through n8n or Make. Once it's running, you won't have to think about it again.

And if you have physical stores? Start paying attention to how much traffic you're missing from location-based searches. The gap might be bigger than you think.

The tools exist. The opportunity is clear. The question is whether you'll capture it before your competitors do.