A Tensor API Lab collection
The word token can refer to a unit of model input or a record in a digital-asset system. This collection keeps those meanings separate and concentrates on the data engineering needed to connect an AI workflow to a traceable source.
For tokenized records, begin with identity and provenance. Decide which network, identifier, document version and observation time define the evidence behind a claim. Only then decide whether summarization, extraction or classification is appropriate. A numerical representation is useful for computation, but it does not establish the truth of the underlying record.
The provenance article develops that workflow from the source record to the model result. Related topic pages explain language-model token usage and the ambiguity of AI-token terminology. These guides focus on technical design and verification, so the examples stay grounded in records and decisions instead of prices or predicted returns. Follow the evidence back to its source whenever a downstream output will be used to make a consequential decision.
1 article in this collection
Build traceable AI data workflows for tokenized assets by preserving source identity, observation times and derivation records. Separate claims from conclusions and evaluate classification with realistic evidence gaps. Connect each model result to the records that supported it.
7 min read
TensorAPI.com