Module Review: LLM Basics

🔑 Key Takeaways

  1. LLMs are Probabilistic: They predict the next token based on statistical patterns learned from massive data, not factual reasoning.
  2. Tokenization: Text is converted into integers (tokens) using BPE. 1000 tokens ≈ 750 words.
  3. Transformer Architecture: The underlying engine that uses Self-Attention to process entire sequences in parallel and understand context.
  4. Parameters: The learned weights of the model. More parameters generally equal higher reasoning capability.

  5. Context Window: The limit on how much text the model can “remember” in a single conversation turn.

Module Review: LLM Basics

[!NOTE] This module explores the core principles of Module Review: LLM Basics, deriving solutions from first principles and hardware constraints to build world-class, production-ready expertise.

1. 🧠 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.

2. 📄 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.

3. 📚 Resources & Next Steps