Inside Large Language Models (LLMs)
Overview
In this lesson, you will learn:
- How LLMs work under the hood using transformers, attention, and tokens.
- The role of data, training, and prediction in language models.
- Beginner-friendly demonstrations of how LLMs “think.”
- Practical exercises to visualize tokenization and next-word prediction.
By the end, you will understand the mechanics of LLMs and how they generate human-like text.
Key Concepts
- Token: Smallest unit of text that a model understands (word, subword, or character).
- Transformer: Neural network architecture that processes sequences efficiently using attention mechanisms.
- Attention: Mechanism that allows the model to focus on relevant parts of the input while predicting outputs.
- Next-Word Prediction: LLMs generate text by predicting the most likely next token.
- Training: Process where the model learns from large datasets to understand language patterns.
Concept Explanation
1. Tokens
- Text is broken into tokens so LLMs can process it.
- Example: “AI is amazing” →
[AI] [is] [amazing] - Tokens allow the model to predict the next word based on context.
2. Transformers
- Transformers handle sequences of tokens efficiently.
- They consist of layers that use attention to weigh the importance of each token relative to others.
- This allows models to capture context over long passages.
3. Attention Mechanism
- Attention decides which parts of the input are important when predicting the next token.
- Example: In “The cat sat on the mat,” attention helps the model understand that “cat” is linked to “sat.”
4. Next-Word Prediction
- LLMs predict text one token at a time using probabilities.
- Each predicted token is added to the context, and the process repeats until output is complete.
Practical Examples
Example 1 – Tokenization
Input: "I love AI"
Tokens: ["I", "love", "AI"]
- Each token is processed by the model individually.
Example 2 – Next-Word Prediction
Input: "The sky is"
Prediction: "blue"
Next token added → "The sky is blue"
- Model predicts the next token based on context.
Example 3 – Attention in Action
- Input: “The dog chased the ball because it was fast.”
- Attention links “it” to “dog” to maintain context in predictions.
Tools for Hands-On Practice
- OpenAI Playground / ChatGPT: Observe how changing prompts affects outputs.
- Hugging Face Transformers: Experiment with tokenization and next-word prediction.
- Google Colab: Run small transformer models for text generation.
- Visual Playground (Interactive tools online): Explore tokenization and attention visually.
Step-by-Step Beginner Activity
- Pick a short sentence (3–5 words).
- Tokenize the sentence manually or using a tool (Hugging Face).
- Predict the next word for each token step-by-step.
- Visualize attention weights if using an interactive transformer tool.
- Observe how the model uses context to generate coherent outputs.
Exercises
- Tokenize the sentence: “AI is transforming the world.”
- Predict the next word for each token manually or with a playground tool.
- Use a transformer visualization tool to see attention patterns for a short paragraph.
- Compare outputs of different prompts in ChatGPT to see how tokenization and attention affect results.
Summary & Key Takeaways
- LLMs process text as tokens, predicting the next token iteratively.
- Transformers and attention mechanisms allow models to handle context effectively.
- Next-word prediction is the core of how LLMs generate human-like text.
- Hands-on experiments with tokenization and attention improve understanding of model behavior.
- Understanding LLM mechanics prepares learners for prompt engineering and AI tool applications.