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.
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
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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.
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Level of control required
Systematic human review before publishing slows down rollout but reduces risk, compared with automatic publishing checked afterwards.
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Catalogue size
Generation and review cost grows with the number of SKUs, even though processing per page stays automated.
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Catalogue update frequency
A catalogue that changes often needs regular regeneration of the FAQ to stay consistent with current attributes.
Frequently asked questions
Is generated content checked before publishing?
Does this approach work on a small catalogue?
Can the model invent false information about a product?
Does the generated FAQ actually help SEO?
Describe your need in one minute
A few targeted questions so I can reply with an estimate rather than another questionnaire.