Utility and energy sector technology leaders, CTOs, and CIOs are grappling with a key conundrum – whether to build their own large language models (LLMs) or leverage external ones, such as ChatGPT and BARD.
Building LLMs requires significant investment and resources, both in terms of infrastructure and talent. On the other hand, using external LLMs may not always fit the specific needs and requirements of an organization.
To help make this decision, let’s look at the pros and cons of each approach. Building your own LLM gives you complete control over the model, and you can customize it according to your unique use case. However, this approach comes with significant costs and takes longer to set up.
Leveraging external LLMs saves time, reduces costs and provides access to cutting-edge technology. Still, it may not capture all the nuances of an organization’s unique context and business requirements.
The decision criteria for choosing between building or leveraging LLMs depend on factors such as time to market, budget, machine learning expertise, and the nature of the application. Organizations with greater financial resources and more significant in-house ML experts may choose to build their LLM. In contrast, smaller firms with limited expertise and resources often prefer to leverage external LLMs.
However, the best decision for many organizations lies in integrating both external and internal LLMs. This approach can help maximize the benefits of both approaches and minimize their shortcomings.
In conclusion, the decision to build or leverage LLMs should be based on the organization’s specific needs and goals. Experts suggest integrating both internal and external LLMs to get the most out of both approaches. What are your thoughts on this matter? Share your experiences and observations with us in the comments section below!
