I get asked a lot: “Can I use generative AI to write my product pages without tanking SEO?” Short answer: yes — but only with a checklist and guardrails. I’ve tested a handful of copy-generators across ecommerce stacks, and the difference between a helpful tool and a traffic-killer usually isn’t the model — it’s the workflow and the quality controls you put around it.
Why a practical checklist matters
AI copy tools can speed up content production, but product pages live at the intersection of SEO, conversion and brand voice. Skip one of those and you either lose search visibility, conversions or trust. I use this checklist whenever I evaluate a tool or set up a new process for writers and product teams. It’s designed to be practical: run a short battery of tests, get objective signals, then decide if a tool fits your stack and risk tolerance.
Quick audit before you start
Before you touch prompts or integrations, answer these quick questions:
These determine the controls you need — e.g., schema output or CMS API access.
The checklist — tests and controls I run
Think of this as operational and technical checks plus quality metrics. I run them in the order below so initial decisions inform later tests.
Test: Can the tool reliably place target keywords into H1, subheads and meta description without keyword stuffing? Try a batch of 10 product briefs with different keyword types (short-tail, long-tail, brand + model).
Why it matters: Search engines still use on-page signals. You want deterministic outputs or templates rather than freeform prose.
Test: Run generated pages through a plagiarism tool and a similarity checker against your site. Also sample the tool-produced content for intra-site duplication (when similar SKUs get near-identical descriptions).
Why it matters: Duplicate content can dilute rankings. Many AI outputs tend to become formulaic; you need variation rules.
Test: Does the tool naturally include semantically related terms (LSI), product specs, and buyer-intent phrases? Use a topical relevance tool (e.g., Clearscope, Surfer) to score outputs.
Why it matters: Rich, relevant copy helps rank for more queries and supports featured snippets.
Test: Can the tool output valid JSON-LD for Product schema (price, availability, sku, brand, reviews) or at least generate the fields your template needs?
Why it matters: Structured data improves SERP features (rich results) and CTR.
Test: If you generate descriptions for similar SKUs, can the tool suggest canonical tags or tailored differentiation notes?
Why it matters: Prevents index bloat and confusion for crawlers.
Test: Ask the tool to produce a spec-heavy paragraph from a provided spec sheet and then to do the same without the spec. Measure mismatch and factual errors.
Why it matters: Generative models invent details. On product pages, invented specs = returns, complaints and reputational damage.
Test: Generate three tone variations (technical, benefit-led, lifestyle) for the same SKU. Measure readability (Flesch scores) and run quick usability testing with team members.
Why it matters: Conversion depends on voice and clarity. You need predictable tone switching.
Test: Can the tool generate meta titles/descriptions that conform to pixel/character limits and include CTA or USPs without truncation?
Why it matters: SERP real estate matters. Truncated titles or missing CTAs reduce CTR.
Test: If you operate in multiple markets, compare native translation vs. machine-assisted output. Do a quality pass with a human translator and measure errors.
Why it matters: Literal translations harm UX and SEO in non-English markets.
Test: Can the tool integrate with your CMS, product feed (CSV), or PIM? Does it support templated outputs (meta, short description, long description, bullet specs)?
Why it matters: The real time-saver is seamless content push and downstream automation (images, tags, schema).
Test: Generate two versions for a sample set and run an A/B test or holdout. Track organic traffic, conversion rate and bounce rate over a 4–8 week period.
Why it matters: SEO changes can take time. You need empirical evidence before scaling.
Test: Confirm tool’s content licensing (can you use commercially? resell? modify?) and whether outputs are used to train the model (privacy concerns).
Why it matters: Legal and compliance risk — particularly for brands with strict IP policies.
Test: Does the tool support scheduled refreshes or bulk updates? Can you pipeline seasonal phrasing or price-change alerts?
Why it matters: Product content ages fast. Having a plan for refreshes preserves SEO relevance.
Useful metrics to capture during evaluation
| Metric | Why it matters |
| Duplicate % vs site | Signals risk of on-site cannibalisation |
| Factual error rate | Returns and legal exposure |
| Avg. meta title length | SERP CTR optimization |
| Topical relevance score | Predicts SEO performance |
| Time-to-publish per page | Operational efficiency |
Prompts, templates and guardrails I actually use
I avoid “generate product description” prompts. Instead I give structured templates. Example prompt I’ve used in pilot tests:
The more structured the input, the fewer hallucinations and the more predictable the output.
Common pitfalls I warn teams about
If you want, I can turn this checklist into a downloadable audit sheet for your team or run a short tool comparison based on three SaaS vendors you’re considering (I’ve tested Jasper, Copy.ai, Writesonic and the OpenAI API in ecommerce contexts). Tell me what you use and I’ll mock up a proof-of-concept audit for two sample SKUs.
