How LLMs Really Work

Behind the Curtain of AI

Large Language Models (LLM) have exploded into our lives, performing feats that feel astonishingly close to magic. They can write code, compose poetry, and debate complex topics. But beneath this magical surface lies a complex, yet understandable, system of engineering and mathematics. Building a strong mental model of how these systems work is the key to using them effectively and responsibly.

You don't need a Ph.D. in machine learning to grasp the core concepts. For anyone with a foundation in computer science, a deep, functional literacy is entirely achievable in less than two years of dedicated learning. This isn't about memorizing algorithms; it's about understanding the "physics" of this new computational world.

How a Model Sees the World

Before an LLM can "think," it must first perceive and represent language in a way a computer can process. This foundational layer governs how all information is handled.

Tokenization: The first step is breaking down human language into pieces the model can understand, called tokens. The phrase "Hello, world!" might be converted into a sequence like [15496, 11, 995, 0]. This single fact explains many of the model's strange quirks. It's why they can struggle with tasks that seem simple to us, like spelling words backward, because they operate on these numerical chunks, not individual letters.
Embeddings: Once text is tokenized, each token is converted into a rich, multi-dimensional vector called an embedding. Think of it as a coordinate on a vast map of concepts. On this map, tokens with similar meanings, like "king" and "queen," are located close together, while unrelated tokens like "king" and "spreadsheet" are far apart. This is the bedrock of the model's ability to understand context and semantic relationships.
Positioning: A sequence of tokens is meaningless without knowing their order. To solve this, models use Positional Embeddings, which are signals added to each token's embedding to provide information about its place in the sequence. This ensures the model can distinguish the meaning between "dog bites man" and "man bites dog."

The Attention Mechanism

This is the revolutionary idea at the heart of nearly every modern LLM. The Transformer architecture, powered by a mechanism called Self-Attention, is what allows the model to intelligently weigh the importance of different tokens in a sequence and draw connections between them.

Self-Attention (QKV): Imagine you are reading a long document. For every word you read, you subconsciously pay more attention to other related words to understand its meaning. Self-attention formalizes this. For every token it's processing (the Query), the model looks at all the other tokens in the text (the Keys) and calculates a relevance score. It then pulls information from the most relevant tokens (the Values) to build a richer, more contextualized understanding of the current token. This is what gives LLMs their remarkable sense of context.
Multi-Head Attention: A single self-attention mechanism is like having one person read a sentence; they'll focus on one set of relationships. Multi-Head Attention improves upon this by running the self-attention process multiple times in parallel. Each "head" acts like a different specialist, independently reading the same sentence but paying attention to different types of relationships. One head might focus on grammatical links, another on semantic similarities, and a third on conceptual opposites. By combining the perspectives of all these different heads, the model builds a much richer and more nuanced understanding of the text than any single head could achieve alone.
KV Cache: This is a crucial performance optimization. When a model generates a response, it would be incredibly inefficient to re-process the entire initial prompt for every new word it writes. The KV Cache acts as a short-term memory, storing the Key and Value calculations from the prompt. This allows the model to "remember" the context without re-computing it. It's the primary reason the first word of a response takes the longest to generate, while the rest stream out much more quickly.

Making Giant Models Practical

As models have grown to trillions of parameters, new architectural innovations have been developed to make them practical to build and run.

Mixture of Experts (MoE): Instead of a single, monolithic brain that is fully active for every task, an MoE architecture works like a team of specialists. A small "router" network sends your request to a small subset of "expert" models best suited for the job. This means that while the model is massive in total, only a fraction of it is used for any given request, making it surprisingly fast and cost-effective.
Attention Variants: The core attention mechanism is powerful but computationally expensive. Techniques like Grouped-Query Attention and Sliding Window Attention are clever modifications that dramatically reduce the amount of computation needed. A sliding window, for example, tells the model to only pay attention to the last few thousand tokens, a key trick for enabling the very long context windows we see today.

How We Steer the Model

Understanding the engine is one thing; knowing how to drive it is another. These are the primary levers used to shape the model's behavior and align it with a specific goal.

Sampling Parameters: These are the creative dials for controlling the model's output. Temperature controls randomness; a low temperature (e.g., 0.2) makes the model more predictable and factual, while a high temperature (e.g., 1.0) makes it more creative. Top-k and Top-p are methods that filter the model's possible next words, forcing it to choose from a shortlist of the most probable options to prevent it from going off on strange tangents.
Pretraining: This is the initial, expensive phase where the model learns grammar, facts, and reasoning by ingesting a huge portion of the internet. The goal here is to create a broad base of general world knowledge. This model is often called a "base model."
Instruction Tuning: A base model knows a lot, but it doesn't know how to be a helpful assistant. Instruction tuning is the process of training the model on a large dataset of questions and high-quality answers. This teaches it to follow instructions and be conversational.
Preference Optimization: This is the final polishing phase, which aligns the model with what humans find helpful and harmless. While the pioneering technique was RLHF (Reinforcement Learning from Human Feedback), the industry has now largely adopted more direct and efficient methods like DPO (Direct Preference Optimization). DPO achieves the same goal without the complexity of training a separate reward model and is now considered the standard. This space continues to evolve with newer techniques, all aimed at making models better aligned with human values.

The Practical Realities

Finally, there's a set of concepts that matter immensely when you're trying to run these models reliably in the real world.

Quantization: This is essentially a compression technique. It reduces the precision of the numbers (weights) used inside the model, like saving a high-resolution image as a slightly lower-quality JPEG. This makes the model file much smaller and faster to run, which is critical for deploying powerful models on consumer hardware like laptops or even phones.
Inference Stacks: When you use a commercial AI product, the model is being run on a highly optimized software stack (like vLLM or TensorRT-LLM). These are the equivalent of high-performance engines for cars, specifically designed to process thousands of user requests simultaneously with minimal latency. They are the unsung heroes that make large-scale AI services possible.
Synthetic Data Generation: This is the practice of using a powerful AI to generate high-quality training examples for another AI. When real-world data is scarce, private, or expensive, we can use a "teacher" model to create new, artificial data to train a "student" model. This creates a powerful feedback loop for continuous improvement.

Conclusion

While the inner workings of LLMs are deeply complex, the core principles are not inscrutable magic. They are an elegant stack of interconnected ideas. Building a strong mental model of these layers, from how the model perceives text to how it's controlled and deployed, is becoming a fundamental literacy in the modern technical landscape. This understanding allows you to move beyond simply using these tools to a place where you can build with them, debug them, and reason about their capabilities and limitations with clarity.