If you're familiar with the world of artificial intelligence, you may have heard of natural language generation (NLG) and GPT-3. These are two of the most popular methods for generating text content, and each has its own advantages and disadvantages.
GPT-3, is a statistical approach that uses large data sets to learn how to generate text. This flexible to use for a wide variety of text types. It is less accurate than NLG as it operates without rules.
NLG, on the other hand, is a rule-based approach that relies on a set of predefined rules to generate text. This means that it can be customized to produce very specific results i.e. product descriptions. It is able to produce very unique texts that are not rewritten from some part of the internet, like GPT-3 does. It is easier to scale to thousands of specific texts than GPT-3.
NLG has a number of use cases, including:
As can be seen NLG has a high usefulness especially in the area of eCommerce copywriting.
An example of this are automated product descriptions.
GPT-3 has a wide range of applications:
In principle, any text can be created, but it is not always correct and structured compared to NLG.
So which approach is better? It really depends on your needs. If you need highly creative results GPT-3 may be the better choice. But if you need something that's more reliable and scalable, then NLG is the better option.
If you need eCommerce texts in high numbers NLG is definitely the right choice. After setting rules, tone-of-voice and structure once, thousands of texts are generated in seconds.
If you want to create eCommerce texts in large quantities, please contact us here.
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