Give the neural network product characteristics and a portrait of the buyer
The neural network does not see your product, so describe it in detail: what it is, what it is made of, dimensions, equipment, how it differs from analogues, who needs it and why. Separately, give a portrait of the buyer: who he is, what problem he solves, what he is afraid of when buying online - the size will not fit, it will be defective, the color will be wrong. It is from these fears that strong lines are born that remove objections. The more texture you give, the less the neural network will fantasize and the more accurately it will hit your client. Collect this data once in a note and then insert it into each new product. A couple of minutes for a detailed description will save you from dozens of edits later.
Build a sales card structure, not a list of facts
The selling card follows logic: a catchy headline with the main benefit, then a block of benefits “in the language of benefits,” then characteristics with facts, then working with objections and a soft appeal. Ask the neural network to collect the text in exactly this structure. The key technique is to translate properties into benefits: not “100% cotton”, but “breathes and does not irritate the skin even in the heat.” Property answers the question “what is it”, benefit answers the question “what will this give me”. Ask to add such a benefit to each characteristic. So the card ceases to be a passport of the goods and begins to answer the buyer’s unspoken question “why do I need this?” Check each paragraph with this question - the excess will be eliminated immediately.
Collect keywords to search within the marketplace
There is a search inside the marketplace, and the card is found according to the words the buyer uses to formulate the request. Ask the neural network to suggest a list of search phrases for your product: synonyms, related queries, different names for one thing - “hoodie”, “sweatshirt”, “sweatshirt with a hood”. Then ask to organically weave the most frequent ones into the title and text, without turning the description into a set of keys separated by commas. Overspam interferes with both the reader and the algorithm. Important: the neural network does not know your real request statistics, so check the final list with the internal analytics of the marketplace or word selection services. The keys should sound natural - then both the person and the search will perceive the card well.
Set the tone without water and remove empty superlatives
“The best quality at an affordable price” is a phrase that the buyer scrolls through without believing a word. Ask the neural network to write specifically: not “high quality,” but what exactly confirms it - fabric density, seam type, service life. Set the tone clearly: calm and confident, without exclamations or bureaucracy, in short sentences. With a separate team, ask to remove the evaluation stamps “unique”, “premium”, “ideal” if there are no facts behind them. A good description can be read out loud, and it sounds like advice from a friend, not like an advertisement from the 2000s. After generation, re-read the text through the eyes of a tired buyer: if a paragraph does not add value, it is unnecessary. Specifics sell better than adjectives.
Check the facts and turn your prompt into a template for hundreds of cards
The last and most important step is the truth. A neural network sometimes confidently attributes properties to a product that do not exist: the wrong composition, a fictitious standard, a non-existent guarantee. Check each characteristic with the real data of the supplier: a lie in the card threatens returns, bad reviews and blocking from the marketplace. When the working text is received, save the request as a template: role, card structure, tone and key requirements, prohibition on making up facts. Then you plug in the characteristics of the new product and get a description in minutes, not in an hour. Essentially, you are mastering prompting - a basic skill in working with neural networks. You can practice formulating such requests in a free introductory lesson.