ai llm books ai/books/handsonlargelanguagemodels
- 1. An Introduction to LLM
- 2. Tokens and Embeddings
- 3. Looking inside LLMs
- 4. Text Classification
- 5. Text Clustering and Topic Modeling
- 6. Prompt Engineering
- 7. Advanced Text Generation Techniques and Tools
- 8. Semantic Search and Retrieval-Augmented Generation
- 9. Multimodal LLMs
- 10. Creating Text Embedding Models
- 11. Fine-Tuning Representation Models for Classification
- 12. Fine-Tuning Generation Models
Extras
BERT-from-First-Principles.pdf
Decoder-Only Transformers (GPT)
Encoder-Only Transformers (BERT)
Mixture of Experts - A First‑Principles Study Note.pdf
Prompt caching - 10x cheaper LLM tokens, but how.pdf
Companion series
The Designing LLM Application chapters parallel this book chapter by chapter:
- 1. Introduction to LLMs — mirrors this book’s overview
- 2. Pre-Training Data — complements the training-data discussion
- 3. Vocabulary and Tokenization — deepens the tokenizer material
- LLM Post Training — extends the fine-tuning chapter