# Generating a relevant FAQ from existing product data

> Generating a relevant FAQ for each product page from the attributes already in the database, rather than writing one by hand product by product, is a type of project I take on for large catalogues.

- Source canonique : [https://allaux.fr/en/expertises/faq-produit-generee-par-ia](https://allaux.fr/en/expertises/faq-produit-generee-par-ia)
- Langue : EN
- Dernière mise à jour : 2026-09-30

## The typical need

An FAQ specific to each product page answers the concrete questions a visitor has before buying, and helps SEO by covering search phrasings that the product page alone doesn't always cover. The problem is volume: on a catalogue of several hundred or thousand SKUs, writing a relevant FAQ by hand for each one simply isn't realistic in human time, and a generic FAQ copied onto every page brings neither value nor real SEO benefit.

## How I approach this kind of work

Generation draws on data already in the database: technical specifications, category, attributes, sometimes existing customer reviews when they contain recurring questions. A language model generates a set of questions and answers consistent with this data, but I never automate publishing without a check: a first pass produces a draft, with human review or an automatic consistency check before it goes live, to catch a generated answer that would be false or misleading about the actual product.

One particular point of vigilance is fabricated content: a language model can produce a plausible but false claim about a product it doesn't actually know beyond the data provided. I strictly limit generation to the data available in the database, without letting the model invent a missing attribute, which greatly reduces this risk without eliminating it completely.

## Factors that affect the estimate

- **Richness of the product data** — A detailed product page with many attributes allows for a more relevant FAQ than a page with little structured information.
- **Level of control required** — Systematic human review before publishing slows down rollout but reduces risk, compared with automatic publishing checked afterwards.
- **Catalogue size** — Generation and review cost grows with the number of SKUs, even though processing per page stays automated.
- **Catalogue update frequency** — A catalogue that changes often needs regular regeneration of the FAQ to stay consistent with current attributes.

## Questions to ask before starting

> Is the product data in the database rich enough to generate a genuinely useful FAQ? Who needs to review the content before publishing, and how often? How do you spot generated content that's gone stale after a product update?

## FAQ

### Is generated content checked before publishing?

It's strongly recommended: an automatically generated draft goes through a review step, either human or an automatic consistency check, before it's made public.

### Does this approach work on a small catalogue?

The benefit is clearest on a large catalogue; on a few dozen SKUs, writing by hand is often quicker to set up than the generation infrastructure.

### Can the model invent false information about a product?

That's the main risk with this kind of generation. I limit generation to data actually available in the database to reduce it, without being able to rule it out completely, which is why review matters.

### Does the generated FAQ actually help SEO?

It can cover search phrasings that complement the product page, provided it's genuinely relevant and not generic; a low-quality FAQ brings no benefit on this front.