This user is right to suspect that manually writing hundreds of product descriptions is not a real solution. And they are also right to question whether a token-limited AI tool can handle the job. The assumption that you must either grind through the work yourself or accept a tool that chokes on large datasets is a false choice, but it is a common one.
The real problem here isn't the volume of data. It is the mismatch between the tool and the task. Many AI-powered spreadsheet add-ons work well for a dozen rows or a single column of text. But when you have hundreds of rows of structured product data, SKUs, prices, specs, categories, spread across multiple columns, the token limits of general-purpose AI models become a hard wall. You cannot generate a coherent, unique description for each row if the model forgets what it saw ten rows ago. The user's frustration is legitimate, and the advice they received to try numerous.ai was reasonable, but it points to a deeper gap: most spreadsheet AI is built for light augmentation, not for batch transformation at scale.
What this means in practical terms is that the user needs a tool that treats the spreadsheet as a structured dataset, not a chat window. The correct approach is to process each row independently, using the column values as structured inputs to a description generator. This is not a token problem, it is a design problem. A tool that can iterate row by row, feeding each product's attributes into a prompt and returning a description, will scale to thousands of rows without hitting a ceiling. The user's Office 365 version supports modern automation features like Office Scripts or Power Automate, which can call an AI service per row. Alternatively, a purpose-built AI spreadsheet tool that processes data in batches, row by row, solves the token bottleneck entirely.
We think this user is on the verge of a breakthrough, not a dead end. They have the data. They know what they want. They just need the right architecture. The solution is not to find a bigger AI model, it is to find a smarter workflow. A tool that understands each row as its own unit, applies a consistent prompt template, and outputs a polished description for every product will turn those hundreds of rows into a finished catalog in minutes. The user should stop looking for a chatbot and start looking for a row-by-row AI processor. That is the practical next step.