Product descriptions used to have one audience and one job: convince a person to buy. They now have three audiences. The shopper still matters most, but search engines and AI assistants also read that text, and they read it differently than a person does. The good news is that writing for all three is not a compromise. The same qualities that help a human decide also help a machine understand.
Here is how we approach product copy on the stores we build.
Lead with the specific, not the vibe
The most common weak description opens with mood. "Elevate your everyday with our premium essentials." A person skims past it. A search engine finds no query to match it to. An AI assistant cannot extract a single fact from it.
Compare that to an opening that states what the thing is and who it is for. "A 200gsm merino wool base layer for cold-weather running, with flatlock seams and a longer back hem." A shopper learns something immediately. The phrase matches real searches. An assistant can pull weight, material, use case, and a construction detail straight out of the sentence.
The rule is simple. The first two sentences should answer what it is, what it is made of, and what it is for. Save the brand voice for after the facts, not instead of them.
Put the attributes where they can be found
Shoppers scan, so do machines, and both reward structure. A wall of prose hides the facts that drive a decision. Break the important attributes into a short list: material, fit, dimensions, care, what is included, what is compatible. This is not just formatting. A bulleted spec list is easier for a person to scan and easier for an assistant to lift cleanly.
This matters even more because of where those attributes end up. The structured data on your product page draws from your real product fields, and an assistant cross-references the visible copy against it. When your description says 200gsm and your structured data agrees, the assistant gains confidence and is more likely to recommend the product. When they disagree, it hedges. Consistency between what a person reads and what a machine reads is its own ranking advantage.
Answer the questions people actually ask
Before writing, I look at three sources of real questions: the store's support tickets, the reviews on similar products, and the autocomplete suggestions in Google and in the assistants themselves. Type your product category into ChatGPT and watch what it asks back. Those follow-up questions are the gaps your description should close.
If shoppers keep asking whether a jacket runs small, the description should say how it fits. If they ask whether a supplement is third-party tested, say so. Answering the real question in the body copy does double duty: it reduces support load and it gives assistants the exact answer to quote when someone asks them the same thing. For the highest-traffic questions, a dedicated FAQ on the product page, marked up as FAQPage schema, turns those answers into something an assistant can surface directly.
Write for the query, not for the keyword
Keyword stuffing is dead for search and useless for assistants, which work on meaning rather than exact strings. What still works is covering the language real people use. A "rain jacket" is also a "waterproof shell," a "packable raincoat," and a "commuter jacket." You do not need to force all of those in. You need to write naturally enough that the genuine variants appear, because both Google and the assistants map between them.
The practical test: read the description aloud. If it sounds like a person describing the product to a friend, it will read well to a shopper and parse cleanly for a machine. If it sounds like it was assembled to hit keywords, both audiences will notice.
A note on writing these at scale
A catalog of fifty products can be written by hand with care. A catalog of five thousand cannot, and this is where most stores either give up or paste in the manufacturer's copy, which every competitor also has. Duplicate manufacturer descriptions are a real SEO liability and they give an assistant no reason to prefer you.
AI is genuinely useful here, with one rule: it drafts, a human edits. A model can produce a structured, fact-forward first draft from your product attributes far faster than a person can, but it should never publish unreviewed. The draft gets the structure and the specifics right; the human adds the judgment, the fit notes, and the brand voice, and catches the occasional confident error. That review step is the difference between scaling quality and scaling sameness.
We build this draft-then-edit workflow into the Shopify stores we run, and it is one of the things AgentReady does for merchants directly: it generates structured, on-brand descriptions and meta from your real product data and shows every change for approval before anything goes live. However you produce the copy, hold it to the same bar. Lead with the specific, structure the attributes, answer the real questions, and write like a person. A description that does those four things works for everyone reading it, carbon and silicon alike.