The GPT Pitfall: Why General LLM Models Fail Specialized Business Needs

Unless you have been living under a rock for the past year and a half, you’ve likely heard the name ChatGPT, if it has not become part of your everyday vocabulary. The chatbot, developed by OpenAI, has risen to be one of the fastest-growing productivity tools in the world, peaking at just under 2 billion monthly active users in April 2024. At the time of writing it is driven by the GPT-4o model, which, while subject to some debate with the recent release of Anthropic’s Claude 3.5 Sonnet model, contends for the top spot in most generalized benchmarks.

The draw, then, for many business owners and organizations wishing to expand their products, tools, and offerings with these generalized models is understandable. Solutions from OpenAI and competitors offer API access to their robust language models at competitive rates, opening the doors for a seemingly endless number of possibilities.

However, while GPT-4o and other general AI models are incredibly robust and versatile, they may not always be the best fit for your organization or business. To better understand why, let’s dive into exactly what these models are, and what they are not.

What General AI Models Are

General AI models like GPT-4o are pretrained Large Language Models (LLMs) that have ingested vast amounts of information from diverse sources. They are designed to understand and generate human-like text based on the input they receive. Their extensive training on broad datasets enables them to provide information and generate content on a wide array of subjects, making them highly versatile tools.

These models excel in generating coherent and contextually appropriate responses, which is ideal for applications such as customer service chatbots, content creation, and virtual assistants. Their broad, generalized form of intelligence allows them to handle multiple tasks without being specifically trained for any single one, offering wide-ranging utility across different domains.

What General AI Models Are Not

Despite their impressive capabilities, general AI models are not a one-size-fits-all solution. They can lack the precision required for specific tasks. While they can provide adequate responses across various domains, they are not finely-tuned for highly specialized tasks.

In technical fields or specific industry applications, their generalized training can lead to less accurate or relevant results. These models rely on cloud-based APIs, meaning they require network communication to process requests and deliver responses. This dependence can result in latency issues, making them slower than on-device models specifically optimized for certain tasks. In real-time applications, this delay can be a significant drawback.

For cloud-based models, using APIs incurs ongoing expenses that can escalate quickly with increased usage. Each API call costs money, and as the demand for AI-driven tasks grows, so does the cost. This can become particularly burdensome for applications requiring frequent AI interactions, making generalized models less economical in the long run.

The need for continuous internet connectivity to access cloud-based models poses a challenge for applications in remote or offline environments. In scenarios where connectivity is unreliable or unavailable, such as rural areas or at sea, this dependency limits the usability and effectiveness of cloud-based general AI models.

Locally-run LLM models attempt to address the cost problem posed by cloud-based solutions but create additional challenges. They currently struggle to compete in most benchmarks with their cloud-based counterparts and suffer from context length constraints and demanding performance requirements. They also require substantial computational power and energy, making them impractical for portable or remote applications.

Case Study: The Fisherman’s Problem

To explore the technical limitations of generalized LLM models, let’s consider a scenario:

The Fisherman’s Problem

You are tasked with developing the latest flagship product from your company: an offboard camera system designed to help fishermen identify fish species in real-time.

This product must:

• Provide real-time processing to identify fish species instantly.
• Operate offline due to the remote locations where fishermen often work.
• Be cost-efficient to minimize ongoing costs.
• Be energy-efficient to function effectively on limited power sources available on fishing boats.

A cloud-based API is immediately ruled out due to the need for continuous internet connectivity and the latency introduced by cloud processing. Local LLM models are also unsuitable as they are not designed for image recognition tasks. Instead, we turn our attention to a Convolutional Neural Network (CNN).

The Solution: Solving the Problem with CNNs

CNNs are specifically designed for image processing tasks and excel in recognizing patterns and features in visual data. This makes them perfect for the task at hand: identifying fish species from camera footage. Unlike LLMs, CNNs can be optimized to run efficiently on limited hardware, ensuring real-time processing without internet connectivity.

Creating a Custom Fish Identification Model

1. Data Collection: Gather a comprehensive dataset of fish images and videos from various angles and lighting conditions. Annotate each image to label the fish species accurately.
2. Data Preprocessing: Normalize and resize images to a consistent format, and augment the dataset with techniques like rotation, flipping, and scaling to improve robustness. Split the data into training, validation, and test sets.
3. Model Selection and Training: Choose a mobile-friendly CNN architecture like MobileNet or EfficientNet, which balances performance and efficiency. Train the model using the preprocessed dataset, leveraging transfer learning to speed up training and enhance accuracy.
4. Model Optimization: Optimize the trained model to run efficiently on the hardware of the offboard camera system. Techniques like quantization and pruning reduce the model size and improve inference speed without significantly compromising accuracy.

Why a Custom CNN Model is Better Suited

A custom CNN model tailored for fish species identification offers targeted performance that a generalized model cannot match. By focusing on recognizing patterns unique to fish, the model achieves higher accuracy and efficiency.

The lightweight design of a custom CNN allows it to run smoothly on limited hardware, ensuring energy efficiency and real-time processing. This is crucial for portable systems like those on fishing boats. Offline functionality eliminates the need for internet connectivity, making the system reliable in remote areas. Additionally, the cost-effectiveness of a custom CNN model avoids the recurring expenses associated with cloud-based APIs.

The Bottom Line

Much like selecting the appropriate tool for a job, choosing the right AI model for a specific task is essential. While the buzz around Large Language Models and “Big Data” is undeniable, it’s important to understand that not all AI solutions are universal.

For specialized tasks, such as the aforementioned real-time fish species identification, a custom CNN is far more effective than a generalized LLM model. Custom solutions offer the precision, efficiency, and cost-effectiveness needed for specific applications which generalized models cannot always provide.

In a landscape flooded with the latest trends, all reaching for the expansive budgets of the wide-eyed, it is important to recognize the value of a tailored approach. Investing in a solution designed to meet your unique business requirements ensures superior performance and long-term value. It’s about making smart choices that align with your specific needs and understanding of what truly drives success in AI applications.