Home Artificial Intelligence ChatGPT Moderation API: Input/Output Control

ChatGPT Moderation API: Input/Output Control

ChatGPT Moderation API: Input/Output Control

Using the OpenAI’s Moderation Endpoint for Responsible AI

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Large Language Models (LLMs) have undoubtedly transformed the best way we interact with technology. ChatGPT, among the many outstanding LLMs, has proven to be a useful tool, serving users with an enormous array of data and helpful responses. Nonetheless, like several technology, ChatGPT isn’t without its limitations.

Recent discussions have dropped at light a vital concern — the potential for ChatGPT to generate inappropriate or biased responses. This issue stems from its training data, which comprises the collective writings of people across diverse backgrounds and eras. While this diversity enriches the model’s understanding, it also brings with it the biases and prejudices prevalent in the true world.

In consequence, some responses generated by ChatGPT may reflect these biases. But let’s be fair, inappropriate responses will be triggered by inappropriate user queries.

In this text, we’ll explore the importance of actively moderating each the model’s inputs and outputs when constructing LLM-powered applications. To achieve this, we’ll use the so-called OpenAI Moderation API that helps discover inappropriate content and take motion accordingly.

As at all times, we’ll implement these moderation checks in Python!

It’s crucial to acknowledge the importance of controlling and moderating user input and model output when constructing applications that use LLMs underneath.

📥 User input control refers back to the implementation of mechanisms and techniques to observe, filter, and manage the content provided by users when engaging with powered LLM applications. This control empowers developers to mitigate risks and uphold the integrity, safety, and ethical standards of their applications.

📤 Output model control refers back to the implementation of measures and methodologies that enable monitoring and filtering of the responses generated by the model in its interactions with users. By exercising control over the model’s outputs, developers can address potential issues equivalent to biased or inappropriate responses.



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