Artificial intelligence (AI) has made incredible strides in recent years, demonstrating the vast potential and power of this transformative technology. In particular, generative AI (Gen AI), powered by large language models (LLMs), promises to unlock even more value from AI by customizing these models to meet specific business needs.

According to a recent McKinsey analysis, Gen AI could boost global productivity by a staggering $2.6 to $4.4 trillion annually across a range of use cases. However, customizing these models to address particular business problems could increase that impact even further - by 15 to 40%. We have already started to see customized models by industry leaders like BloombergGPT in finance, Google MED-PaLM in healthcare, and JPMorgan DocLLM in banking.

Let’s see how customized LLMs differ from commercial ones, why they are a game-changer, and why they are the future of AI.

Off-The-Shelf LLMs: Pros & Cons

Out-of-the-box commercial LLMs like ChatGPT offer some clear advantages - namely, convenience and a load of capabilities at your fingertips. These general-purpose models don’t require any training data or expertise to use and can handle a wide range of conversational and generative tasks very well. For many companies exploring Gen AI's potential, this accessibility is excellent for rapid prototyping and innovation.

However, as time goes on, the limitations of one-size-fits-all LLMs will start to emerge. As you scale up applications, problems like high costs, slow processing, high latency, and lack of privacy and ownership will become more apparent.

In addition, commercial LLMs’ generalization becomes a drawback compared to customized solutions made for specific goals. For example, a customized LLM for marketing will consistently outperform multi-purpose models like ChatGPT in creating ads, landing pages, and lead-gen emails.

When we talk about customization or specialization, we refer to the fine-tuning techniques where LLMs (pre-trained models) are trained on your business data (or any specific data) to achieve specific tasks.

Essentially there is a trade-off between generalization and customization. Off-the-shelf LLMs are versatile but generalists, while customized models are less versatile but specialized; they excel in specific jobs, optimize computational resources, and are more cost-effective.

By narrowing the focus of LLMs, efficiency and performance improve dramatically. Customized models become accessible without extensive data or resources.

Customization is a Game Changer

At their core, LLMs are sophisticated AI-driven models capable of processing and generating human-like text. Through the process of fine-tuning, you refine and customize these models for specific tasks.

What makes customization or fine-tuning unique is that it enables any business to create its specialized task-specific models without the need for extensive data or computing power.

By fine-tuning, you not only enhance the performance, speed, and cost-effectiveness of these models but also adapt them to your unique business needs. You move from a large general model to a small specialized one.

Customization also speeds up the development process. You can use general models as prototypes and then refine them by using data collected from actual use. It remains a powerful technique to leverage the capabilities of LLMs and make them more accessible and efficient for various applications.

A recent survey compared ChatGPT with smaller fine-tuned models, and the results were surprising. ChatGPT only won 34 out of 151 comparisons (22.5%). In other words, it was outperformed by fine-tuned models in 77.5% of tasks.

Power of Customized Models

Customized or specialized models are like tailored outfits of LLMs adapted to a specific task or domain using a small amount of data. They are powerful because, given their smaller size and specialization, they require less data, less computational resources, and less inference time than large general models.

The fine-tuning technique updates the parameters of a model using a task-specific dataset, to make the model more relevant and accurate for the task. It can be done with a few thousand examples or even less, depending on the quality of the data and the complexity of the task.

What sets customized LLMs apart is their compelling features that align with your business needs:

Fast & Efficient

They exhibit unparalleled agility, offering swift and efficient performance. They are nimbler and faster at generating results compared to large models which makes them ideal for tasks that demand quick responses.

Specialization

Fine-tuning tailors LLMs to be very effective at specific tasks rather than general capabilities. This specialized optimization boosts accuracy and relevance for intended uses.

Privacy Protection

With less data and access needed for narrow functions, fine-tuned models present less privacy risks than commercial LLMs. They operate with smaller datasets and are designed for specific functionalities. This design choice contributes to robust data protection. It mitigates the risk of data leaks or exposures, a crucial feature for businesses.

Resource Efficiency

Smaller models require fewer computing resources for both training and deployment. This makes them easily adaptable and compatible with a wide range of devices and IT systems.

Adaptive Learning

Customized LLMs learn rapidly from ongoing real-world usage. As they interact within defined domains, the models continuously improve through experiential learning.

To Customize or Not To Customize?

In short, it depends primarily on your objective. But first, start by assessing data availability, quality, size, the tasks to accomplish, and the delicate balance between cost and performance.

In general, large general models work well in handling diverse tasks that demand extensive general knowledge and versatility, particularly in scenarios like prototyping or when data is scarce.

On the flip side, opt for small, customized, and specialized models when your focus is on tasks requiring high efficiency, heightened privacy, speed, and cost-effectiveness. The key is aligning the model with your precise needs for optimal results.

Looking Forward

Customized LLMs, also called specialized and task-specific, are the key to unlocking AI potential across all industries. While general LLMs sparked initial excitement, the real power lies in the fine-tuning process.

For most business applications, off-the-shelf LLMs fall short. The paradigm shift towards fine-tuning is proving superior in speed, efficiency, accuracy, cost, and privacy compared to relying on commercial LLMs.  And that’s exactly what your business needs to succeed and stay ahead of the competition.

While general models play a crucial role in prototyping and data generation, it's the fine-tuned, customized LLMs that take center stage. They present a clear path forward, offering a nuanced solution that aligns with the specific needs of businesses.

Companies that leverage customized LLMs will gain a distinct edge, while laggards risk losing ground. Instead of relying on commercial LLMs' APIs, the strategic imperative is to customize your own model. Make it a core priority to transform your business. Start here by consulting with our team of experts to chart the path forward.