Product Q&A for AI Shopping: Conversational Attributes

When a shopper asks ChatGPT "will these boots fit wide feet" or asks Google AI Mode "which of these blenders crushes ice", the assistant looks for products whose information answers the question. Product Q&A is that information: the questions shoppers ask before they buy, and the direct answers published where the assistant reads them. Fit and sizing, what the product works with, what it is made of, how to care for it, how fast it ships and how returns work are the questions that come up most across categories.

Buyers describe the same thing in their own words: "we are adding FAQs to product detail pages to try and help with the LLMs." The point of the term is to treat those answers as product information, written once from your sources and carried to each place an assistant reads, the same way an attribute is.

FAQPage structured data tells a search engine that a page contains questions and answers. It is a wrapper. The answers themselves are the product information, and they have to be visible on the page for two reasons. Google asks that structured data match the content a visitor sees, and the assistants that crawl your pages read the rendered text, so the visible answer is what counts and the markup describes it.

Three places hold the same answer:

• The product page. A visible Q&A block, an expanded specification table or a sizing and care section. This is what ChatGPT, Perplexity and Copilot crawl.
• Google Merchant Center. Google added optional conversational attributes to the product data specification so merchants can describe the nuances AI systems ask about, alongside the standard attributes.
• The OpenAI product feed. The feed specification carries titles, descriptions, attributes and Q&A-style fields that ChatGPT shopping reads for eligible merchants.

Keep the three consistent, and the page version is the one to write first. See product schema markup for the markup side.

A good answer is short, specific and traceable to a source. "Runs about half a size large, order your usual size for a snug fit" comes from the fit data and the return reasons. "Compatible with 1/4 inch and 3/8 inch hoses" comes from the specification sheet. The sources, in order of trust, are the product record and PIM, supplier and manufacturer documents, the existing product page and manuals, customer service questions, and reviews read as signals of what to answer, with each fact confirmed against a source.

Claims matter most in regulated categories. A supplement brand is asked about sleep and stress, a skincare brand about medical conditions, and the honest answer has to stay inside what the brand may say. Product Q&A handles this by answering the shopper's underlying question with the facts the brand can state, for example ingredients, dosage format and certifications, within the claims guidelines the brand approves up front. See product attribute completeness for the measurement behind the gaps.

Paz tracks the questions shoppers ask across ChatGPT, Google AI Mode, Gemini, Perplexity and Copilot, and reads the answers next to your catalog, Google Merchant Center and analytics. Where an assistant leaves a product out, our experts trace it back to the question nobody answered on the page or in the feed, draft the answer from your sources and in your voice, and publish it to supported storefront fields and feed attributes within the guidelines you approve. If your sources disagree, we check with you before changing anything, and PIM corrections go to your PIM owner so the record stays authoritative.

Appearances are then tracked per assistant against your baseline, so you can see which answers moved. This is part of the product content service, and the measurement side is covered in brand mention, product card and citation.