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OpenAI’s Modified AI Offering Receives a Lukewarm Response from Developers

The company checks the customization process and the data used very carefully using a moderation API and the GPT-4 powered moderation system.

ShahZaib Ahmed



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OpenAI has recently released the option of customizing GTP-3.5 Turbo. This enables artificial intelligence developers to improve performance concerning particular. They can do this by utilizing dedicated data. Nonetheless, developers have displayed both enthusiasm and criticism regarding the development.

OpenAI’s Customized AI Feature Gets Mixed Reactions from Developers

The company stated that via the procedure of customization, developers can modify the GPT-3.5 Turbo’s capabilities to meet their requirements. In this respect, a developer can customize GPT-3.5 Turbo for developing a customized code. In addition to this, the developers can effectively sum up legal documents. This requires the use of a dataset that is sourced from the business operations of the consumer.

The recent declaration has ignited a cautious reaction from the developers. An X user called Joshua Segeren commented on this by saying that the release of the respective feature is deficient. As per the X user, this does not denote a thorough fix. The consumer discussed the observations, use of vector database concerning semantic searches, improvement prompts, or evolution to GPT-4.

An X User Criticizes the Latest Feature Saying It Is Not a Complete Solution

The X client added that these things often provide better results as compared with custom training. Apart from that, there are some crucial aspects such as ongoing maintenance charges and setup. The feature has great significance regarding developers and enterprises targeting to create personalized consumer communication. For example, platforms can customize the model to coordinate with the voice of their band.

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In this way, they can guarantee that the chatbot expresses a consistent tone and a personality that matches with the brand. The system endeavors to identify and eliminate likely unsecured training data. Hence, it ensures that the customized output is in line with the traditional security norms of OpenAI. It also indicates that the platform controls the data provided by the consumers to a certain extent.

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