Module Review: LLM Basics

Note

This module explores the core principles of Large Language Models (LLMs). We will derive solutions from first principles, understanding how simple probabilistic “next token prediction” scales up through the Transformer architecture to create systems capable of world-class, human-like reasoning.

1. 🔑 Key Takeaways & Analogies

Understanding LLMs requires shifting from deterministic programming logic to probabilistic thinking. Think of an LLM as a highly-read Improv Actor: it doesn’t have a structured “database of facts” to query; instead, it looks at the script so far (the prompt) and guesses the most plausible next word based on everything it has ever read.

  1. LLMs are Probabilistic: They predict the next token based on statistical patterns learned from massive data. Analogy: It’s like the autocomplete on your phone, but trained on the entire internet, with a vastly larger context window.
  2. Tokenization (The Model’s Alphabet): Text is converted into integers (tokens) using BPE (Byte Pair Encoding). A token isn’t always a full word. Rule of Thumb: 1000 tokens ≈ 750 words. For example, “Hamburger” might be split into “Ham”, “bur”, “ger”.
  3. Transformer Architecture (The Engine): The underlying engine that uses Self-Attention to process entire sequences in parallel and understand context. Analogy: Imagine reading a book where you can instantly draw lines between a pronoun on page 10 and the noun it refers to on page 1. That’s Self-Attention.
  4. Parameters (The Brain’s Synapses): The learned numerical weights of the model. More parameters generally equal higher reasoning capability and broader knowledge representation.
  5. Context Window (Working Memory): The limit on how much text the model can “remember” in a single conversation turn. If the context window is 128k tokens, the model completely forgets token 128,001.

Interactive: Next Token Prediction Simulator

Next Token Prediction Simulator

Click a token to build the sentence. LLMs calculate the probability distribution for the next token.

The robot

2. 🧠 Interactive Flashcards

Test your knowledge. Click a card to flip it.

What is the core function of an LLM?
To predict the next token in a sequence based on probability.
What is a Token?
The basic unit of text for an LLM (word, char, or subword). ~0.75 words.
What does "Temperature" control?
The randomness of the output. Low = Focused/Deterministic, High = Creative/Random.
What is Self-Attention?
A mechanism allowing words to "look at" other words in the sentence to determine context and meaning.
What is Hallucination?
When an LLM generates confident but factually incorrect information.
What is BPE (Byte Pair Encoding)?
A tokenization method that merges frequent character pairs into single tokens to optimize vocabulary size.

3. 📄 Cheat Sheet

Term Definition
Inference The process of running the model to generate text.
Training The process of teaching the model using massive datasets (expensive, one-time).
Fine-Tuning Adapting a pre-trained model to a specific task (cheaper).
Context Window The memory limit of the model (e.g., 128k tokens).
Parameter A numerical weight in the neural network.
Transformer The neural architecture that enables parallel processing and attention.

4. 📚 Resources & Next Steps