Tokenizer
A tokenizer splits raw text into the discrete tokens a model actually sees — bytes, characters, or learned subwords — and defines the vocabulary the softmax is over.
Definition
Tokenizer refers to bytes, characters, or learned subwords — and defines the vocabulary the softmax is over. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It binds model output to retrieval, tools, or evaluation so answers stay grounded instead of free-floating. When bytes, characters, or learned subwords — and defines the vocabulary the softmax is over shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what tokenizer is saying. If bytes, characters, or learned subwords — and defines the vocabulary the softmax is over moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Measure grounding rate, latency, and failure modes under missing context — not demo chat quality alone. Prefer a short written null hypothesis for Tokenizer: what would falsify the current reading in the next window?
Ask the macro AI about this object
Opens Copilot with Codex + RAG context, or send the object into Alpha Factory intake.