Transformers and Large Language Models
Trace how text becomes tokens, contextual representations, next-token probabilities, decoded outputs, and evaluated language-model behavior.
Begin chapter- 01How LLMs Generate Text
See how a language model predicts, selects, and appends one token at a time.
- 02LLM Tokenization
Understand how text becomes versioned token IDs, context usage, and model cost.
- 03Transformers
The attention mechanism that powers modern AI.
- 04Embeddings & Context Windows
Learn how models represent text and how finite context changes application design.
- 05LLM Decoding & Sampling
Compare greedy decoding, temperature, and top-p through visible probability changes.
- 06LLMs
Large Language Models and the GPT architecture.
- 07LLM Limitations & Grounding
Recognize unsupported output and design evidence, citations, abstention, and evaluation.