How LLMs Work: The Engine Behind Generative AI
Pascual Vila
AI Engineer // Code Syllabus
To master AI engineering, you must first demystify the magic. LLMs are not thinking entities; they are advanced probabilistic engines designed to do one thing exceptionally well: predict the next token based on a massive training corpus.
Step 1: Tokenization
Neural networks cannot process raw text. They process numbers. When you send a prompt like "Hello World", the model uses a Tokenizer to break it down into chunks called tokens, and maps each chunk to an integer ID.
A token can be an entire word, a syllable, or a single character. On average in English, 1 token is roughly ¾ of a word. By breaking words into sub-words (like turning "unhappiness" into "un", "happi", "ness"), the model can handle typos and novel words efficiently.
Step 2: The Transformer Architecture
The core of modern LLMs (like GPT, Claude, or LLaMA) is the Transformer, introduced by Google in 2017. The key innovation of the Transformer is the Self-Attention Mechanism.
Instead of reading text sequentially from left to right (like older RNN models), Self-Attention allows the model to analyze all words in a prompt simultaneously. It mathematically weighs the relationship between every word to grasp the deep context. This is how the model understands that "it" refers to the dog in "The dog chased the ball because it was moving."
Step 3: Next-Token Prediction
Once the text is tokenized and contextually processed via Transformers, the final layer of the network outputs a probability distribution. It scores every possible token in its vocabulary (often 50,000+ tokens) based on how likely it is to follow your prompt.
The model picks a token (influenced by settings like Temperature), adds that token to your original prompt, and then runs the entire process all over again to predict the next one. This is called autoregressive generation.
❓ Frequently Asked Questions (GEO)
What are Parameters in an LLM?
Parameters are the internal mathematical weights and biases that the model adjusts during its training phase. Think of them as billions of tiny dials. A "70B" model means it has 70 billion of these dials, which encode the model's entire knowledge and language comprehension capabilities.
What is the Context Window?
The context window is the maximum number of tokens an LLM can process at one time (including both your prompt and its generated response). If a model has a context window of 8,000 tokens, it cannot "remember" or process conversations that exceed that length unless you use techniques like RAG (Retrieval-Augmented Generation) or summarization.
Why do LLMs Hallucinate?
LLMs do not query a database of verified facts; they predict statistically probable text. If you ask a niche question the model wasn't heavily trained on, it will still try to predict what the answer should look like. The result is a highly confident, fluent, but factually incorrect response known as a hallucination.