The rise of “layer” applications built on top of generative AI models has enabled businesses to offer more engaging and personalized experiences to their users, but without collecting feedback from users about the outputs of their system, they’re missing out on valuable opportunities to improve their offerings. In this article, we’ll explore the realities of why including feedback loops are essential for anyone building something on top of a 3rd party AI model.
Feedback loops are a critical component of training AI Models
Recently, there has been a wave of applications that are building their products and features as a “layer” on top of third-party AI models like ChatGPT, Midsommar, and others. This trend has allowed companies to offer more engaging and personalized experiences to their users without having to build their own models. The flexibility and adaptability of generative AI models like ChatGPT make it a powerful tool for a wide range of applications. These “layer” applications typically act as the “front end” that the user interfaces with, supplementing and augmenting user requests with subject matter expertise or context with the prompt. The application then sends a prompt to the 3rd party AI model, and the model generates a response based on its language model. This response is then sent back to the application, which can further process or display it to the user. However, because these applications don’t directly train the model, the vast majority of them do not include any form of feedback loop. This means that they are missing out on valuable opportunities to improve their offerings and may entirely miss it if their system is producing unsafe or unideal responses to their users.
Feedback Loops in Practice
Thankfully, not everyone has abandoned the feedback loop. ChatGPT itself uses a feedback loop to improve its language model by analyzing user input and responses. Whenever a user interacts with the feedback loop, the user’s input and response are recorded and used to refine the model. This can look like explicit interactions, such as clicking the “thumbs up” or “thumbs down” icons next to ChatGPT’s output, or even something less explicit, like whether or not the user copied the output, regenerated it, rewrote the prompt, and more. This feedback loop allows ChatGPT to learn from its mistakes and improve the accuracy and quality of its responses over time. By continually analyzing user interactions, ChatGPT is able to adapt to new data and improve its performance, resulting in a better user experience and increased engagement.
While ChatGPT is a great example of an AI that incorporates user feedback into its language model, the vast majority of these new “layer” applications built on top of 3rd party AIs like ChatGPT don’t have any feedback loop or feedback mechanism. This is a concerning trend because it means that users are not able to provide valuable feedback to improve the accuracy, efficiency, and user-friendliness of these applications. Without feedback loops, not only are they missing out on valuable opportunities to improve their offerings, but also that they may entirely miss it, if their system is producing false, destructive, or even dangerous outputs to their users.
Feedback loops aren’t just for training AI models, it’s also for training AI systems on top of 3rd party
Even if the feedback that is collected never makes it back to OpenAI to improve the underlying ChatGPT model, it can still be utilized in many ways. One of the most straightforward examples of how a feedback loop can improve the system is be prompt-tweaking. While a 5–10 word phrase might be great for finding a link or answer on Google, ChatGPT prompts are often many sentences long, crafted through trial and error, to produce the type of output that the creator is looking for. With qualitative and quantitative feedback from users on the other end of those outputs, these “Layer” applications can incrementally change, or even AB test the prompts that are generating these outputs, to address the feedback that their customers are leaving.
Prompt AB Testing, Powered by Feedback Loops, Are Coming to the Most Advanced “Layer” Applications
It’s About Maturity, Not Just Improvement
Having a feedback loop in an AI application isn’t just good for improving the product, it is also a sign of a mature product, because it means that the organization managing it has done its due diligence in safeguarding the product, and cares about improving it. A feedback loop demonstrates that the organization is committed to continually improving its AI system, based on the input and feedback of its users. It also shows that the organization is willing to listen to its users and take their needs and concerns seriously. By implementing a feedback loop, organizations can build trust with their users, and demonstrate that they are committed to delivering a high-quality product that meets their needs. Overall, a feedback loop is an essential component of any AI system, and is necessary for ensuring that the outputs of the system is accurate, valuable, and user-friendly. And while a feedback loop can be built into any product In order for these applications to grow into the mature products that businesses, and not just individuals, will adopt, these types of safeguards and commitments to improvement are essential.
A feedback loop is an indicator of a mature AI product
Feedback loops are a critical component of any AI system, including ones that are built on top of 3rd party models. By collecting and analyzing user feedback, organizations can continually improve the quality and accuracy of their systems, resulting in a better user experience and increased engagement. As we continue to embrace the power of generative AIs and integrate them into our daily lives, it is more important than ever to prioritize the inclusion of feedback loops and ensure that these systems are continually improving and adapting to meet the needs of their users.