Choosing the Right LLMs to Solve Your Analytics Challenges

The LLM (large language models) market is growing exponentially and is set to quadruple in size by 2029. Some LLMs are incredibly successful, however, not all LLMs are created equal, and Generative AIs’ usefulness is heavily dependent on the quality of data used to train it. Because of this, the likelihood of your work use case being answered accurately depends on finding the right LLM for the data model concerned.  

Generalized LLMs vs. Specialized LLMs 

There are many generalized LLMs on the market trained with large data sets, such as ChatGPT or GitHub Copilot, which can cover a broader scope of the market. Despite this, if you rely on one LLM it might not be able to help you with your specific use case or industry.  

On the flip side, verticalized LLMs are trained in distinct industry terms and metrics, and the use of specialized LLMs and verticalized LLMs can deliver sophisticated and accurate answers to your data queries. While these LLMs perform better, they are still in their infancy.  

Some industries haven’t even created specialized LLMs yet, but they are coming and your LLM strategy should be ready for them, for when they are developed. The Pyramid Platform, for example, already supports multiple LLMs, users can even attach a specific LLM to a specific model. With this capability, Pyramid Generative BI capabilities enables your analytics to make use of existing GenAI in addition to preparing your analytics for the GenAI of tomorrow.  

Employing a multi-LLM strategy gives you greater flexibility when it comes to machine learning and natural language processing. You do not want to be locked into one perspective or limited to the scope of one LLM, no matter how extensive the data sets on which that LLM was trained.  

Ultimately, using the right combination of LLMs helps you solve your use case, letting your users arrive at a more informed decision.