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Five modules take you from the foundations of neural networks through model mechanics, grounded systems, quality engineering, and controlled autonomy. Each lesson builds on the last.

Module 0基礎
Foundations
3 lessons
1. 00aHow machines learn from examples
2. 00bThe transformer insight
3. 00cFrom architecture to LLM
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Module 1モデル力学
Model mechanics
5 lessons
1. 01From text to the next token
2. 02Context is a finite workspace
3. 03Long context is not perfect recall
4. 04Embeddings: useful geometry, not truth
5. 05Sampling: how one continuation is chosen
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Module 2基盤システム
Grounded and adapted systems
3 lessons
1. 06Hallucinations and reliability boundaries
2. 07RAG: retrieve evidence before generation
3. 08Choosing prompting, RAG, and fine-tuning
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Module 3品質工学
Quality engineering
2 lessons
1. 09Evals: define and measure useful behavior
2. 10Testing probabilistic software
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Module 4制御された自律
Controlled autonomy
2 lessons
1. 11Prompt injection and secure tool use
2. 12Agentic workflows with bounded autonomy
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