Understanding the LLM Twin Concept and Architecture

By the end of this book, we will have walked you through the journey of building an end-to-end large language model (LLM) product. We firmly believe that the best way to learn about LLMs and production machine learning (ML) is to get your hands dirty and build systems. This book will show you how to build an LLM Twin, an AI character that learns to write like a particular person by incorporating its style, voice, and personality into an LLM. Using this example, we will walk you through the complete ML life cycle, from data gathering to deployment and monitoring. Most of the concepts learned while implementing your LLM Twin can be applied in other LLM-based or ML applications.

When starting to implement a new product, from an engineering point of view, there are three planning steps we must go through before we start building. First, it is critical to understand the problem we are trying to solve and what we want to build. In our case, what exactly is an LLM Twin, and why build it? This step is where we must dream and focus on the “Why.” Secondly, to reflect a real-world scenario, we will design the first iteration of a product with minimum functionality. Here, we must clearly define the core features required to create a working and valuable product. The choices are made based on the timeline, resources, and team’s knowledge. This is where we bridge the gap between dreaming and focusing on what is realistic and eventually answer the following question: “What are we going to build?”.

Finally, we will go through a system design step, laying out the core architecture and design choices used to build the LLM system. Note that the first two components are primarily product-related, while the last one is technical and focuses on the “How.”

These three steps are natural in building a real-world product. Even if the first two do not require much ML knowledge, it is critical to go through them to understand “how” to build the product with a clear vision. In a nutshell, this chapter covers the following topics:

• Understanding the LLM Twin concept
• Planning the MVP of the LLM Twin product
• Building ML systems with feature/training/inference pipelines
• Designing the system architecture of the LLM Twin

By the end of this chapter, you will have a clear picture of what you will learn to build throughout the book.