Lost Control

Welcome to the chapter where we dive into the tricky world of losing control when working with AI language models. You'll discover how these models can sometimes behave unpredictably, making it hard to predict their actions and ensure they deliver what you expect.

In this part, we’ll uncover why these issues occur by examining how model definitions are integrated into system prompts. We’ll also look at the impact of different model sizes and settings on their ability to understand instructions and provide accurate responses.

We'll conduct experiments with various models like Qwen 2.4 3B and Llama 3.2 3B, observing how they handle specific tasks and questions related to a TypeScript-based backend framework called PURISTA. You’ll see patterns in how these models interact with tools and generate answers, revealing insights into their decision-making processes.

Understanding the behavior of AI language models is crucial for building reliable applications. We'll discuss the phenomenon known as "Exponential Cascading Inaccuracies," where small inaccuracies can lead to significant errors over time, especially in smaller models. This chapter will equip you with knowledge on how to test and evaluate models effectively, ensuring they meet your requirements.

Join us as we explore these fascinating challenges and learn how to navigate them successfully!