4.1.1 Tokenization and Subwords¶
Why This Sub-Part Matters¶
Tokenization and Subwords is the working skill inside Token and Context Mechanics that helps you build the stage artifact, An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases, while collecting enough evidence to trust the result. A sub-part is now a folder so longer topics can grow without forcing everything into one huge page.
Study Pages¶
| Page | Purpose |
|---|---|
| Deep Dive | Full explanation, mechanisms, examples, and failure modes. |
| Examples and Practice | Worked exercises, project drills, and self-check prompts. |
Core Ideas¶
- Define Tokenization and Subwords in plain language before naming tools or frameworks.
- Connect it to the stage artifact: An LLM fundamentals notebook comparing models, tokenization, structured outputs, embeddings, costs, and failure cases.
- Measure it with: token counts, truncation risks, cost, and latency
- Name at least one failure mode, because real AI engineering is mostly controlled failure reduction.
- Keep the first implementation small enough to inspect by hand before scaling it.
How to Study It¶
- Read this overview and write the concept in your own words.
- Read the deep dive and identify the input, transformation, output, and failure mode.
- Complete the examples and practice page.
- Add one measurement using: Report token counts, truncation risks, cost, and latency.
Completion Standard¶
- I can explain Tokenization and Subwords without naming a tool first.
- I can connect it to the stage artifact.
- I can show a small artifact, measurement, or test.
- I know how it fails and what I would inspect first.
Return to 4.1 Token and Context Mechanics.