Product Description and Ad Generation With Generative Models

What are Product Description and Ad Generation and Why Use Generative AI?

Product description generation is about automatically generating a product description based on keywords. This can be very convenient when you have hundreds or thousands of products and you can't afford to regularly write appealing descriptions for each product. Ad generation is about automatically generating an advertising based on keywords. It can be very useful if you need to write a high volume of ads on a regular basis, without sacrificing your original and genuine tone. Generative models are perfectly suited for these tasks because they are very good at generating relevant and appealing descriptions based on raw information, in many languages. This is pure original content so there is no risk of duplicated content.

For example pass the following instruction to the AI: Generate a product description that contains the following keywords: shoes, women, $99. The AI model could return something like this:

Of course you can ask for much more advanced descriptions. For example you can change the tone, or you can ask the AI to generate longer descriptions.

GPT-J, GPT-NeoX, LLaMA 2, Dolphin, and ChatDolphin are the most advanced alternatives to GPT-4 and ChatGPT. These models are so big that they can adapt to many situations, and perfectly sounds like a human. For advanced use cases, it is possible to fine-tune GPT-J and Dolphin (train them with your own data), which is a great way to perform product description and ad generation that are perfectly tailored to your company/product/industry.

Performing Product Description and Ad Writing With Generative Models%}

{% tr Building an inference API for blog post generation based on generative models is a necessary step as soon a you want to use blog post generation in production. But building such an API is hard... First because you need to code the API (easy part) but also because you need to build a highly available, fast, and scalable infrastructure to serve your models behind the hood (hardest part). It is especially hard for machine learning models as they consume a lot of resources (memory, disk space, CPU, GPU...).

Such an API is interesting because it is completely decoupled from the rest of your stack (microservice architecture), so you can easily scale it independently, and you can access it using any programming language. Most machine learning frameworks are developed in Python, but it's likely that you want to access them from other languages like Javascript, Go, Ruby...