AI Product Descriptions for Marketplaces Without Invented Specs

AI Product Descriptions for Marketplaces Without Invented Specs

To write AI product descriptions for marketplaces without inventing information, start from the real spec sheet, give the model only that data, and forbid any attribute that is not in it. Then adapt the text to each marketplace's rules and review it before publishing. AI helps you write and standardize, but responsibility for the accuracy of the listing stays with your company.

Key takeaways

• Description quality depends on spec sheet quality: bad data going in comes out as convincing, wrong text.
• The model should receive structured fields and an instruction to use only what is in them, with an explicit option to say that a piece of information was not provided.
• Each marketplace has its own rules for titles, fields and allowed terms. Check the current seller guide instead of trusting the model's memory.
• A sample review that checks each field against the spec sheet is worth more than a generic reread.
• Start with one product line, measure the rework, and only then expand to the whole catalog.

Why does AI invent attributes in product descriptions?

A language model is trained to produce plausible text. If you ask for a description of a Bluetooth speaker and provide only the name and brand, it fills the gaps with what usually appears for similar products: water resistance, battery life, Bluetooth version. You supplied none of that. The text comes out fluent, well organized and full of attributes the product may not have.

For a catalog, this is the central risk. An invented attribute leads to returns, complaints, marketplace disputes and, depending on the case, trouble under Brazil's consumer protection code. This is not a rare defect you can ignore. It is the expected behavior when data is missing and the instruction does not prevent the model from filling in.

The good news is that process controls the problem. The idea of anchoring the answer in a trusted data source is the same one behind retrieval-augmented generation (RAG): the model writes, but the information comes from a base you control. In your case, that base is the spec sheet.

How to prepare the spec sheet before asking for any text

Before you open the AI tool, organize the data. Many companies have product information scattered across spreadsheets, supplier PDFs, the ERP and WhatsApp messages. If that data is contradictory, the description will inherit the contradiction. If your spec sheets are in PDF, see how to extract data from PDFs with AI, with fields, rules and human review before generating any text.

A minimum checklist for each product:

• Identification: name, brand, model, internal code and barcode when available.
• Objective attributes: dimensions, weight, material, color, voltage, capacity, compatibility, in the format and unit the supplier uses.
• Package contents: what comes with the product and what does not (batteries, cable, manual).
• Warranty and certifications: only what appears in a document, with the source noted.
• Usage restrictions and safety warnings provided by the manufacturer.
• Empty fields marked as empty, never filled in by assumption.
• Source of each data point: supplier, manual, label or your own measurement.

The last item looks bureaucratic, but it is what lets you audit later. If someone questions a claim in the listing, you know where it came from.

Step by step: generating descriptions from the spec sheet

The flow below works for a spreadsheet with a few dozen items or for an integration with your catalog system.

• Step one: export the sheet in separate fields (one product per row, one column per attribute), with no free text mixed in.
• Step two: define the output format: title, highlight bullets, long description and, if needed, extra marketplace fields.
• Step three: write the fixed model instructions, with rules for tone, audience and prohibitions.
• Step four: send one product's sheet at a time, together with the instructions, and ask for the description.
• Step five: also ask for a list of spec sheet fields that went unused or had no information, to reveal gaps.
• Step six: compare the text with the sheet, field by field, before approving.
• Step seven: store the approved version, the date and the data source.

The fixed instructions in step three are the most important part. A practical set of rules:

• Use only the information present in the spec sheet provided.
• If an attribute is not in the sheet, do not mention it and do not infer it.
• Do not use superlatives or promises the sheet does not support, such as the best, the toughest or guaranteed forever.
• Do not mention competitors or compare with other brands.
• Keep numbers and units exactly as they appear in the sheet.
• At the end, list the fields that were not used.

Working with a few products at a time also helps. Large batches increase the chance that the model mixes attributes between similar items, such as two versions of the same device. If your volume is large, understanding the context window helps you decide how much to send in each call.

How to adapt the text to each marketplace's rules

Once you have a faithful description, adaptation comes next. Mercado Livre, Amazon, Shopee, Magalu and your own store tend to differ in title structure, required fields, special characters, list formats and banned terms. These rules change over time, so the right source is always each platform's current seller guide. Do not ask the model to remember the rules. Provide the official excerpt as part of the instruction, or keep your own reviewed summary with the date of the last check.

An organized way to work is to keep one base description and derive variations:

• Base description: neutral, complete, faithful to the sheet, with no channel-specific formatting.
• Channel variation: the same content reorganized to fit that marketplace's fields and limits.
• Own-store variation: room to say more about use and care, with attention to what helps organic search.

A few practical decisions help you choose what goes where:

• If a field is structured (color, voltage, material), fill it with the sheet data and do not repeat it in free text in a conflicting way.
• If the platform restricts terms, such as health claims or performance promises, remove them from the text and do not try to work around the rule.
• If the marketplace requires specific images and attributes for a category, treat that as part of the spec sheet, not as a job for the text.
• If you are unsure about a rule, go with the more conservative text and confirm with platform support or the seller guide.

For your own store, the same care for useful content applies to search. Google's guidance on helpful, reliable, people-first content discourages pages mass-produced without real value. Identical, generic descriptions across hundreds of pages do not help the buyer. If visibility in AI assistant answers matters to you, also read about how to appear in AI answers.

Illustrative scenario: from an incomplete sheet to a reviewed listing

The example below is an illustrative scenario, unrelated to any real company.

A houseware store sells a thermal bottle. The supplier sheet lists a stainless steel body, a plastic lid, height and diameter, and a note that it must not go in the dishwasher. There is no data on how long it keeps temperature, and nothing on whether it is BPA free.

The model receives the sheet with the rule to use only those fields. It returns a title, bullets and a description that mention the stainless steel, the measurements and the dishwasher warning. At the end, it lists as missing: temperature retention time and the lid's BPA status. Without that rule, the text would probably promise many hours of cold drinks, something nobody verified.

The person in charge of the catalog sees the gap list and decides: ask the supplier for the information in writing. Until it arrives, the listing goes out without the promise. When the data comes in, the sheet is updated and the text is generated again. The marketplace variation uses the same content, reorganized to match that platform's guide.

The gain here is not speed. It is that the gap became visible before it turned into a complaint.

How to review, measure and scale without losing control

Human review does not have to mean reading everything. Define an objective check: for each text in the sample, the reviewer compares numbers, units, materials, package contents and any warranty or certification claim against the sheet. If the text contains something that is not in the sheet, it is an error, even if it sounds right. Sensitive items, such as products with health claims, child safety implications or regulatory requirements, deserve full review. This connects to what not to delegate to AI in your business.

To measure simply, build a fixed set of test products with known answers and rerun it whenever you change the instruction or the model. OpenAI's guide to evals describes the practice of building repeatable evaluations. In a catalog context, an evaluation can be as simple as counting how many texts mentioned an attribute missing from the sheet.

The method we apply in AI projects for companies fits here without major changes:

• Map: survey how descriptions are written today, who approves them, where the sheets are and how much rework there is now.
• Limit: define what data the model receives, which fields it may use and what it must never claim. If the sheet includes supplier data under contract, review what leaves the company.
• Pilot: pick one product category, generate with logs, review by sample and agree on a success metric, such as the rate of texts approved without correction.
• Transfer: hand the documented flow to the team, with versioned instructions and a clear decision to expand, adjust or stop.

If your marketing team also researches competitors to position listings, keep that step separate and handle sources carefully, as described in competitor analysis with AI. Copying text from another listing carries legal and quality risk.

Frequently asked questions

Can AI write product descriptions on its own, without review? For simple, low-risk items, the level of review can be lower, done by sampling. Even so, without some check against the spec sheet, invented attributes can reach the listing. Items involving safety, health or regulatory requirements should get a full review.

Can I ask the AI to look up specifications on the internet? It is possible, but it reintroduces the risk of incorrect data or specs from a different model of the same product. The safest practice is to use the manufacturer's or supplier's sheet as the source and, if the AI brings in anything external, treat it as a suggestion to be confirmed in an official document.

Can the same text be used on every marketplace? It can serve as a base, but it usually needs adjustments to the title, structured fields and allowed terms on each platform. Checking the current seller guide is more reliable than reusing a finished text, because the rules change.

Do I need to worry about LGPD when generating product descriptions? In general, a product spec sheet does not contain personal data, so the risk is lower than in customer support. Still, avoid sending the model spreadsheets with customer data or confidential commercial terms from suppliers, and check the tool's policy on retention and training with your data.

Sources

• AWS: What is retrieval-augmented generation
• Google Search Central: helpful, reliable, people-first content
• OpenAI: evals guide