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Table of contents
Tracing AI
A clear path to understanding how modern AI assistants work, reason, fail, and can be used well
Read each section in order. Every title can be opened as a TheoryTrace document.
- Cover1
- Copyright2
- How to read this book3
- Introduction4
- Chapter 1: What an AI Assistant Is5
- Chapter 2: From Rules to Learning Systems6
- Chapter 3: Data, Patterns, and Generalization7
- Chapter 4: The Core Idea of Machine Learning8
- Chapter 5: Neural Networks from First Principles9
- Chapter 6: Language as Data10
- Chapter 7: The Transformer Architecture11
- Chapter 8: What Large Language Models Learn12
- Chapter 9: How an AI Assistant Produces an Answer13
- Chapter 10: Instruction Following and Alignment14
- Chapter 11: Prompts, Context, and Conversation15
- Chapter 12: Reasoning, Planning, and Tool Use16
- Chapter 13: Knowledge, Uncertainty, and Hallucination17
- Chapter 14: Memory, Retrieval, and Grounding18
- Chapter 15: Evaluation and Benchmarks19
- Chapter 16: Safety, Bias, and Responsible Use20
- Chapter 17: How to Work Effectively with AI21
- Chapter 18: Building a Mental Model of the Assistant22
- Conclusion23