Large Language Model-Based Solutions

4Model Selection and Alternatives

WHAT'S IN THIS CHAPTER?

• Introduction to Model Selection
• The Role of Compact and Nimble Models
• Domain‐Specific Models
• The Power of Prompting with General‐Purpose Models

INTRODUCTION TO MODEL SELECTION

In the rapidly evolving field of artificial intelligence and machine learning, the selection of appropriate models has become a pivotal aspect of successful implementation in various applications. This chapter delves into the intricacies of model selection, focusing on the trade‐offs between compact, nimble models and their larger counterparts, the emergence of domain‐specific models, and the criticality of inference hyperparameter optimization.

The landscape of AI models has expanded dramatically, offering a spectrum from compact, nimble models to massive, complex ones. Each model type presents unique advantages and challenges, making the selection process critical for efficiency, cost‐effectiveness, and performance optimization. This diversity in model choices allows for tailored solutions across different domains and applications, from mobile applications requiring low latency to large‐scale data analysis demanding high computational power.

MOTIVATING EXAMPLE: THE TALE OF TWO MODELS

To illustrate the importance of model selection, consider the contrasting scenarios of a startup developing a mobile health application and a large corporation analyzing vast amounts of financial data. The startup might opt for a compact model due to ...