LLM product descriptions aren't the point

Every ecommerce vendor I talked to in the last six months has asked about AI-generated product descriptions. It’s the first thing people think of when they hear “AI for ecommerce.” And sure, it works. You can point an LLM at a spreadsheet of product attributes and get readable copy back in seconds.

But that’s not the interesting part. The interesting part is what happens when you get the inputs right.

The copy quality depends on your data, not your model

I’ve seen merchants run the same GPT-4 prompt against two different product catalogs. Same model, same prompt template. One produced descriptions that were ready to publish. The other produced generic fluff that read like every other product page on the internet.

The difference was the input data. The first merchant had clean, structured attributes: material composition, dimensions, use cases, care instructions, competitive positioning notes. The second had a title and a manufacturer part number.

This shouldn’t be surprising. LLMs are pattern matchers. Give them thin inputs, you get thin outputs. The model isn’t doing the heavy lifting. Your product data is.

At Creatuity, we’ve started telling clients: don’t budget for the AI tool until you’ve budgeted for the data cleanup. A merchant with 50,000 SKUs and inconsistent attributes will spend more time fixing AI-generated descriptions than they would have spent writing them the old way.

Where this actually saves time

The real win isn’t replacing copywriters. It’s handling the long tail. Most mid-market retailers have a small team writing descriptions for new or high-traffic products, while the rest of the catalog sits with manufacturer-supplied copy or nothing at all.

Here’s what I’ve seen work:

• Bulk enrichment for stale catalogs. One merchant we worked with had 30,000 SKUs with original manufacturer descriptions from 2018. They ran an LLM pipeline that rewrote everything for consistency and SEO in about two days of processing time. A human editor spot-checked 5% and flagged maybe 3% of those for revision. That’s a project that would have taken a copy team six months.
• Localization at scale. If you’re selling into multiple markets, translating descriptions isn’t enough. You need localized copy that accounts for regional phrasing, measurement units, and cultural context. LLMs handle this better than pure translation services because they can rewrite rather than translate word-for-word.
• A/B copy variants. Generate three versions of a description, test them, keep the winner. This was impractical before because writing variants was expensive. Now the cost is near zero, so the only cost is the test infrastructure.

What still needs humans

The parts where AI falls short are the same parts that always mattered most. Brand voice. Nuanced claims. Anything regulated. Anything that could get you sued.

I watched a retailer run AI descriptions across a supplement catalog and nearly publish claims about FDA approval that didn’t exist. The model confidently hallucinated regulatory language because the prompt didn’t explicitly constrain it. A human reviewer caught it in QA, but it was close.

The other gap is differentiation. If every merchant uses the same model with the same generic prompt, you get the same descriptions. I can spot AI-generated ecommerce copy from across the room now, and I bet your customers can too. It has a rhythm. Short opening sentence. Two or three feature bullets. A closing line about how this product will improve your life.

Your competitors are generating the same copy. The only way to stand out is to invest in better inputs, custom prompts tuned to your brand voice, and human editors who catch the generic stuff before it goes live.

The pipeline matters more than the model

So here’s what I’d focus on if you’re building this out:

1. Clean your product data first. Consistent attributes, complete fields, accurate specs. This pays dividends everywhere, not just in AI copy.
2. Build prompt templates for your categories. A power tool description needs different structure than a skincare product. One-size-fits-all prompts produce one-size-fits-all copy.
3. Add a human QA step. Even if it’s spot-checking 5-10% of output, you need eyes on the result. Especially for anything making claims.
4. Measure the output. Track conversion rates, organic traffic, and return rates for AI-generated descriptions versus human-written ones. If the AI copy isn’t performing, the fix is almost always in the inputs, not the model.

AI-generated product descriptions are table stakes at this point. The merchants who get real value from them are the ones who treat them as a data problem, not a content problem.

How are you handling product copy across your catalog? I’m curious whether most teams have moved to AI-generated or are still doing it by hand for the top sellers.