I've recently spent a considerable amount of time working with GPT-3. During this period, I've explored autonomous agents and their applications, as well as worked on applications that use the openAI API for text editing. Throughout these experiences, I've noticed a recurring challenge: it's remarkably difficult to predict the format of GPT-3's outputs reliably.

Prompt engineering can help guide AI models like GPT-3 towards the desired output. However, from my observations, it seems that prompt engineering isn't scalable. No matter how well-crafted your prompt may be, you can never be 100% sure it will generate the same type of response every time it is used. This unpredictability is in stark contrast to traditional programming, where specific steps and input formats lead to consistent and predictable results. For example, when you write a Python function that returns a dictionary, you can be sure that every time the function is used, the output will indeed be a dictionary.

The response format conundrum

The response format plays a crucial role in building complex applications with multiple interconnected components. Ensuring seamless communication between components is critical, and this is why we have formats like JSON, which enable one component to send information or responses to another component with a consistent and well-defined structure.

For an application to function effectively, it is essential that the input received by one component from another is in a reliable and predictable format. In the case of GPT-3 or GPT-4 based agents, the most reliable output you can typically expect is text. Unfortunately, getting a JSON-like output reliably is not feasible with these AI models. This limitation poses a significant challenge for developers, as they must find ways to efficiently parse text and account for potential inconsistencies.

Adding to the complexity, the text output from GPT-3 can be in various languages, and since GPT-3 primarily focuses on predicting the next word in a sequence, it doesn't pay much attention to the overall formatting or structure of the text. As a result, parsing text from GPT-3 or GPT-4 is no easy task, and their inability to provide consistent and reliable outputs becomes a significant obstacle for developing complex applications based on these AI models.

Unreliable autonomous agents

The uncertainty in GPT-3's output format poses a significant challenge for applications based on it, particularly for autonomous agents. When designing fully autonomous agents that can complete tasks and communicate with one another, the inherent unpredictability of responses must be factored in. This unpredictability means that any application using GPT-3 or GPT-4 (and potentially beyond) will, at some point, be unreliable. So far, I haven't found a foolproof way to mitigate this issue.

As I continue to explore the fascinating world of AI and prompt engineering, I'll be keeping a close eye on how to tackle the challenges of response format unreliability.

What do you think about all this ? Let me know in the comments.