How to Explore the Limitations of ChatGPT

When discussing the limitations of ChatGPT or any artificial intelligence model, five key factors must be considered.

These can be split into two main groups: intrinsic model limitations and usage limitations. Here’s a simple breakdown of how these limitations work.

Intrinsic Model Limitations

These are technical limitations that come from the AI model itself. They include:

• Model Architecture: The design of the model has a significant impact on what it can do. For example, just as we don't expect a simple model like logistic regression to handle complex tasks, we can't expect AI models to overcome their built-in limits, no matter how much data we give them.
• Training Data: The amount and quality of data used to train the model also play a significant role. However, adding more data doesn't always improve the model once it reaches a certain point.
• Cost of Training and Using the Model: Even if there’s plenty of data to train the model, the cost of doing so might be so high that it doesn’t make sense from a business perspective.

Usage Limitations

These are limitations that come from how we use the AI model:

• Integration into Applications: It is crucial how easily the model can be used or integrated into other systems. Large language models (LLMs) like ChatGPT perform well here because they can process natural language, making them flexible for different applications. However, integrating them can still require a lot of engineering work.
• The Prompts We Use: The way we communicate with the model through prompts also affects its performance. Poorly structured or unclear prompts can lead to poor results, even from an advanced model.

How Model Architecture Limits Capabilities

The model’s architecture limits its capabilities. Just like a logistic regression model cannot solve complex problems, AI models have technical limits. These limitations might not always be evident for large language models (LLMs) like ChatGPT because we are early adopters.

Today, many people are exploring new ways to use LLMs, like creating AI agents to perform tasks such as coding. This is a great way to test the true capabilities of these models. However, it’s unclear if the limitations faced will be overcome with the current industry trends because they depend on the factors defined above.

How Data Quality Affects AI Capabilities

Different AI models need different amounts and types of data to learn. The model may struggle to improve if there isn't enough data or the quality is poor. Although there’s a lot of data available on the internet today, regulations in the future could make it harder to access all that data.

That said, the current abundance of data usually means this isn’t a huge problem for now.

The Cost of Training and Using AI Models

Even with unlimited data, the cost of training and using AI models can be high. Models like ChatGPT require significant computational resources, making them expensive to run. For businesses, the question is whether the benefits are worth the cost in the long run.

The Ability to Integrate AI into Applications

One of the significant strengths of LLMs is their ability to process natural text, which makes them easy to integrate into many applications. However, this still requires much engineering work, especially for more complex systems. While the industry is progressing, integrating AI effectively can still be challenging.

A Personal Example: Testing ChatGPT with Complex Applications

Recently, I tried using GPT Engineer, a tool that uses ChatGPT to write code. It worked well for simple applications, but the model started making mistakes as the project became more complex. When I tried to fix one part of the code, it often broke something else.

There could be several reasons for this:

• Limited Context: The code became too big, and the model may have struggled because it couldn’t keep all the necessary information in memory. This is a current limitation, but it might improve in future versions.
• Training Data: The model might not have been trained on enough coding data to handle complex tasks effectively.
• Lack of Collaboration Features: The application doesn’t yet have the ability to collaborate with team members, which would be helpful for complex tasks. Solving this would require significant resources and engineering skills.
• Prompt Quality: We might not have used the best prompts to get the most out of the model.
• Model Capabilities: The task might be too complex for the model to handle because it couldn’t understand the relationships between different parts of the code.

My Thoughts

It’s essential to stay informed about this technology and keep learning about its progress and limitations.

From a user’s perspective, integration and prompt usage limitations are the easiest to overcome and don't require significant breakthroughs in the industry.

However, if we overcome those and still can’t get the application to work as expected, we may be facing intrinsic limitations. These are much harder to solve and will depend on unpredictable advancements in the AI field.