Chapter 1: What Are LLMs? From Transformers to Titans

Large Language Models (LLMs) have quickly become the engines driving today’s AI revolution, shaping how we write, code, search, and even reason with machines. In this first chapter, we explored what LLMs are, why they matter, and how different architectures and scaling approaches have defined their evolution.

We began with a journey across the leading families of LLMs. OpenAI’s GPT showed the world the raw power of scaling up transformer-based architectures, while Meta’s LLaMA line changed the game by releasing open-weight models that engineers could run and fine-tune on their own. We also looked at Claude, Anthropic’s alignment-focused model built on the idea of constitutional AI; Gemini, Google DeepMind’s multimodal powerhouse; Mistral, the efficient open-source disruptor; and DeepSeek, a cost-effective entrant bringing strong performance-per-dollar. Each of these models reflects a different set of priorities—openness, safety, efficiency, or multimodality—giving engineers today more choices than ever before.

Next, we examined the three major transformer architectures. Decoder-only models, like GPT and LLaMA, specialize in generative tasks by predicting the next token one step at a time. Encoder-decoder models, such as T5, shine in sequence-to-sequence work like translation and summarization, where input and output are distinct. Finally, the Mixture-of-Experts (MoE) design, used by Mixtral and others, introduced a scalable way to activate only a subset of parameters per token, making trillion-parameter models computationally feasible. Understanding these designs is crucial, since each architecture carries implications for performance, cost, and real-world deployment.

We then turned to scaling laws, the hidden rules behind LLM growth. The Kaplan scaling laws (2020) revealed that model performance improves predictably as you increase parameters, data, and compute. However, DeepMind’s Chinchilla paper (2022) corrected this by showing that many models were “undertrained”—they had too many parameters for the amount of data they saw. The Chinchilla insight emphasized balance: for compute to be used efficiently, models need around 20 tokens per parameter. This discovery reshaped how the industry approaches training, explaining why modern models like LLaMA and Mistral are smaller in size but trained on much larger datasets.

Taken together, this chapter highlighted that LLMs are not just bigger neural networks—they are carefully engineered systems whose capabilities depend on architecture, training regimes, and scaling strategies. Whether you’re choosing between open-source and proprietary APIs, planning fine-tuning strategies, or estimating compute costs, the lessons from GPT to Chinchilla guide the way.

In the next chapter, we’ll peel back another critical layer: tokenization and embeddings—the hidden vocabulary LLMs use to transform human language into the numerical representations that make all of this possible.

Large Language Models (LLMs) have quickly become the engines driving today’s AI revolution, shaping how we write, code, search, and even reason with machines. In this first chapter, we explored what LLMs are, why they matter, and how different architectures and scaling approaches have defined their evolution.

We began with a journey across the leading families of LLMs. OpenAI’s GPT showed the world the raw power of scaling up transformer-based architectures, while Meta’s LLaMA line changed the game by releasing open-weight models that engineers could run and fine-tune on their own. We also looked at Claude, Anthropic’s alignment-focused model built on the idea of constitutional AI; Gemini, Google DeepMind’s multimodal powerhouse; Mistral, the efficient open-source disruptor; and DeepSeek, a cost-effective entrant bringing strong performance-per-dollar. Each of these models reflects a different set of priorities—openness, safety, efficiency, or multimodality—giving engineers today more choices than ever before.

Next, we examined the three major transformer architectures. Decoder-only models, like GPT and LLaMA, specialize in generative tasks by predicting the next token one step at a time. Encoder-decoder models, such as T5, shine in sequence-to-sequence work like translation and summarization, where input and output are distinct. Finally, the Mixture-of-Experts (MoE) design, used by Mixtral and others, introduced a scalable way to activate only a subset of parameters per token, making trillion-parameter models computationally feasible. Understanding these designs is crucial, since each architecture carries implications for performance, cost, and real-world deployment.

We then turned to scaling laws, the hidden rules behind LLM growth. The Kaplan scaling laws (2020) revealed that model performance improves predictably as you increase parameters, data, and compute. However, DeepMind’s Chinchilla paper (2022) corrected this by showing that many models were “undertrained”—they had too many parameters for the amount of data they saw. The Chinchilla insight emphasized balance: for compute to be used efficiently, models need around 20 tokens per parameter. This discovery reshaped how the industry approaches training, explaining why modern models like LLaMA and Mistral are smaller in size but trained on much larger datasets.

Taken together, this chapter highlighted that LLMs are not just bigger neural networks—they are carefully engineered systems whose capabilities depend on architecture, training regimes, and scaling strategies. Whether you’re choosing between open-source and proprietary APIs, planning fine-tuning strategies, or estimating compute costs, the lessons from GPT to Chinchilla guide the way.

In the next chapter, we’ll peel back another critical layer: tokenization and embeddings—the hidden vocabulary LLMs use to transform human language into the numerical representations that make all of this possible.

Large Language Models (LLMs) have quickly become the engines driving today’s AI revolution, shaping how we write, code, search, and even reason with machines. In this first chapter, we explored what LLMs are, why they matter, and how different architectures and scaling approaches have defined their evolution.

We began with a journey across the leading families of LLMs. OpenAI’s GPT showed the world the raw power of scaling up transformer-based architectures, while Meta’s LLaMA line changed the game by releasing open-weight models that engineers could run and fine-tune on their own. We also looked at Claude, Anthropic’s alignment-focused model built on the idea of constitutional AI; Gemini, Google DeepMind’s multimodal powerhouse; Mistral, the efficient open-source disruptor; and DeepSeek, a cost-effective entrant bringing strong performance-per-dollar. Each of these models reflects a different set of priorities—openness, safety, efficiency, or multimodality—giving engineers today more choices than ever before.

Next, we examined the three major transformer architectures. Decoder-only models, like GPT and LLaMA, specialize in generative tasks by predicting the next token one step at a time. Encoder-decoder models, such as T5, shine in sequence-to-sequence work like translation and summarization, where input and output are distinct. Finally, the Mixture-of-Experts (MoE) design, used by Mixtral and others, introduced a scalable way to activate only a subset of parameters per token, making trillion-parameter models computationally feasible. Understanding these designs is crucial, since each architecture carries implications for performance, cost, and real-world deployment.

We then turned to scaling laws, the hidden rules behind LLM growth. The Kaplan scaling laws (2020) revealed that model performance improves predictably as you increase parameters, data, and compute. However, DeepMind’s Chinchilla paper (2022) corrected this by showing that many models were “undertrained”—they had too many parameters for the amount of data they saw. The Chinchilla insight emphasized balance: for compute to be used efficiently, models need around 20 tokens per parameter. This discovery reshaped how the industry approaches training, explaining why modern models like LLaMA and Mistral are smaller in size but trained on much larger datasets.

Taken together, this chapter highlighted that LLMs are not just bigger neural networks—they are carefully engineered systems whose capabilities depend on architecture, training regimes, and scaling strategies. Whether you’re choosing between open-source and proprietary APIs, planning fine-tuning strategies, or estimating compute costs, the lessons from GPT to Chinchilla guide the way.

In the next chapter, we’ll peel back another critical layer: tokenization and embeddings—the hidden vocabulary LLMs use to transform human language into the numerical representations that make all of this possible.

Large Language Models (LLMs) have quickly become the engines driving today’s AI revolution, shaping how we write, code, search, and even reason with machines. In this first chapter, we explored what LLMs are, why they matter, and how different architectures and scaling approaches have defined their evolution.

We began with a journey across the leading families of LLMs. OpenAI’s GPT showed the world the raw power of scaling up transformer-based architectures, while Meta’s LLaMA line changed the game by releasing open-weight models that engineers could run and fine-tune on their own. We also looked at Claude, Anthropic’s alignment-focused model built on the idea of constitutional AI; Gemini, Google DeepMind’s multimodal powerhouse; Mistral, the efficient open-source disruptor; and DeepSeek, a cost-effective entrant bringing strong performance-per-dollar. Each of these models reflects a different set of priorities—openness, safety, efficiency, or multimodality—giving engineers today more choices than ever before.

Next, we examined the three major transformer architectures. Decoder-only models, like GPT and LLaMA, specialize in generative tasks by predicting the next token one step at a time. Encoder-decoder models, such as T5, shine in sequence-to-sequence work like translation and summarization, where input and output are distinct. Finally, the Mixture-of-Experts (MoE) design, used by Mixtral and others, introduced a scalable way to activate only a subset of parameters per token, making trillion-parameter models computationally feasible. Understanding these designs is crucial, since each architecture carries implications for performance, cost, and real-world deployment.

We then turned to scaling laws, the hidden rules behind LLM growth. The Kaplan scaling laws (2020) revealed that model performance improves predictably as you increase parameters, data, and compute. However, DeepMind’s Chinchilla paper (2022) corrected this by showing that many models were “undertrained”—they had too many parameters for the amount of data they saw. The Chinchilla insight emphasized balance: for compute to be used efficiently, models need around 20 tokens per parameter. This discovery reshaped how the industry approaches training, explaining why modern models like LLaMA and Mistral are smaller in size but trained on much larger datasets.

Taken together, this chapter highlighted that LLMs are not just bigger neural networks—they are carefully engineered systems whose capabilities depend on architecture, training regimes, and scaling strategies. Whether you’re choosing between open-source and proprietary APIs, planning fine-tuning strategies, or estimating compute costs, the lessons from GPT to Chinchilla guide the way.

In the next chapter, we’ll peel back another critical layer: tokenization and embeddings—the hidden vocabulary LLMs use to transform human language into the numerical representations that make all of this possible.