Large Language Models (LLMs) have undoubtedly transformed the way we interact with technology. ChatGPT, among the prominent LLMs, has proven to be an invaluable tool, serving users with a vast array of information and helpful responses. However, like any technology, ChatGPT is not without its limitations.
Recent discussions have brought to light an important concern — the potential for ChatGPT to generate inappropriate or biased responses. This issue stems from its training data, which comprises the collective writings of individuals 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 real world.
As a result, some responses generated by ChatGPT may reflect these biases. But let’s be fair, inappropriate responses can be triggered by inappropriate user queries.
In this article, we will explore the importance of actively moderating both the model’s inputs and outputs when building LLM-powered applications. To do so, we will use the so-called OpenAI Moderation API that helps identify inappropriate content and take action accordingly.
As always, we will implement these moderation checks in Python!
It is crucial to recognize the significance of controlling and moderating user input and model output when building applications that use LLMs underneath.
📥 User input control refers to the implementation of mechanisms and techniques to monitor, 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 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 such as biased or inappropriate responses.