start with the idea before the implementation.
the mechanisms you need to reason about.
Normalization
Normalization is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Word/subword units
Word/subword units is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Special tokens
Special tokens is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Unknown terms
Unknown terms is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
turn the lesson into evidence.
build a tokenizer baseline
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
compare word and subword behavior
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
measure vocabulary size
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
handle punctuation and casing
ask cortex to test me →explain OOV behavior
ask cortex to test me →preserve important technical symbols
ask cortex to test me →what usually goes wrong.
destructive normalization
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
language bias
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
inconsistent train/inference rules
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short Tokenization engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.
Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.