Separate product pages, support, and recommendations
In an online store, I would not combine product pages, support, and recommendations into one prompt. Product pages need facts, support needs escalation, and recommendations need stock and compatibility.
The mistake I would remove first: The most expensive hallucination is not a weak headline—it is promising specs, warranty, or availability that are not in the catalog.
What to prepare and what result to expect
- Outcome: Product pages get richer, support answers with facts, and recommendations do not suggest out-of-stock items or products with the wrong specs.
- Prepare a product export with sku, name, attributes, price, stock, brand, category, and updated_at.
- Keep source data and access rights separate from the output so you can audit what the ecommerce AI workflows did.
- Test a product with incomplete attributes, zero stock, and a question that is not covered in the catalog.
Connect AI to the catalog without invented specs
- Sync the catalog
Connect the source where sku and stock are the source of truth. AI must not store price and availability in memory or in a static prompt.
Проверьте: The answer to a price question matches the catalog at request time.
Если не сработало: Limit the workflow to descriptions without price and stock.
- Define a product page template
Allow the model to fill description, benefits, and FAQ only from attributes. Forbid adding certification, warranty, or materials that are not in the data.
Проверьте: Every claim can be found in the source product record.
Если не сработало: Add a missing_facts field and send the product to editorial review.
- Separate support from recommendations
For FAQ, use catalog search; for recommendations, use filters by category, budget, and compatibility. Do not let the model change an order with a single button.
Проверьте: A “will this work for me?” question gets an attribute-based explanation, not ad copy.
Если не сработало: Show three matching products and ask the customer to choose.
- Run spot checks
Before publishing, review random product pages from each category and refresh them after attribute changes. Put errors into a separate queue.
Проверьте: A catalog update does not bring back an old description and price.
Если не сработало: Publish as draft and require manual approval.
Where AI should get product facts
Pass the model sku, title, category, attributes, price, stock, brand, warranty, and updated_at. Price and stock should be requested from the catalog at response time, not stored in the prompt. If an attribute is empty, return missing_fact.
For recommendations, apply ordinary filters first: category, budget, compatibility, stock. AI can explain the choice in plain language, but it must not change an order, discount, or delivery address with a single button.
- An out-of-stock product must not appear in “buy now.”
- Keep new product pages in draft before publishing.
How to audit the catalog in an hour
Pick three products from each category: complete, with empty attributes, and with zero stock. Compare every fact in the answer with the source. Log errors in a table with sku, field, source_value, ai_value, correction, and reason—that is faster than endlessly rewriting the prompt.
I review product pages after a catalog update
After a price, stock, or attribute change, run a spot check: three product pages per category—complete, with a gap, and with zero stock. Keep `sku`, `field`, `source_value`, `ai_value`, `correction`, and `reason` in the table so you can see whether the source or the model instruction is broken.
Before publishing, compare the old and new product page. If AI added a warranty, material, or discount that is not in the catalog, set `needs_review` and do not send the copy to the store. Recommendations may explain a choice, but changing an order, discount, or address should stay a separate, reviewable operation.
How to choose a safe workflow for a store
| Criterion | Question | Good sign |
|---|---|---|
| Input | What exactly enters the ecommerce AI workflows? | Prepare a product export with sku, name, attributes, price, stock, brand, category, and updated_at. |
| Action | What is the system allowed to do on its own? | Only prelisted actions, without access to the entire account |
| Check | How do you know the result is acceptable? | Test a product with incomplete attributes, zero stock, and a question that is not covered in the catalog. |
| Failure | Where does an unclear case go? | Show “we will confirm stock/specs” and create a task for a teammate—do not substitute a similar product at random. |
What should change after setup
Product pages get richer, support answers with facts, and recommendations do not suggest out-of-stock items or products with the wrong specs.
Why an AI seller starts promising extras
Generating copy from a stale export.
Adding benefits that are not in the attributes.
Mixing recommendations with order changes.
Not reviewing product pages with empty fields.
When store AI needs a separate data layer
Bring in a specialist if the store is large, the catalog changes often, you use personalized recommendations, or you integrate with payments and orders.
What to check on a product with incomplete data
Who is this approach for when working with ecommerce AI workflows?
In a store, it is better to split three different jobs: product pages, answers to questions, and recommendations. Each has its own data, failure modes, and way to verify results.
Where should you start if everything is still manual?
Prepare a product export with sku, name, attributes, price, stock, brand, category, and updated_at.
How do you check that the setup will not cause harm?
Test a product with incomplete attributes, zero stock, and a question that is not covered in the catalog.
What should you do with an unclear result?
Show “we will confirm stock/specs” and create a task for a teammate—do not substitute a similar product at random.
