TensorAPI.comA place for useful questions
Meet the Tensor API Lab.
TensorAPI.com is an independent field guide for developers making sense of tensors, LLM workflows and the data that connects them. The aim is simple: make the important distinctions clear enough to use.
Clear concepts, practical decisions.
AI projects bring several vocabularies into the same conversation. A token might mean a model input unit or an asset record. An API might expose numerical arrays, a message interface or a deployment operation. A successful response might mean a request was accepted, while the application still needs to decide whether the output is useful.
Tensor API Lab follows those boundaries. We explain the underlying concepts, show small examples and connect them to questions a developer can inspect: what shape is expected, what evidence supports a label, which component owns a transformation, and what should happen when an answer is uncertain.
How the Lab approaches a topic.
Name the boundary
Identify the caller, the input and the responsibility of the component behind the interface.
Make examples explicit
Use small, illustrative values and contracts so the reasoning can be checked without assuming a running service.
Follow the evidence
Link to an official reference for the documented behavior and separate it from implementation advice.
Include the awkward case
Consider missing data, incompatible shapes, ambiguous labels and output that needs review.
Read in the order that helps you.
The twelve topic guides provide a quick orientation to each subject. The ten original articles in the Tensor API Lab go further, with worked reasoning, practical checks and common mistakes. Categories group related workflows; tags connect a concept across different articles.
If you are new to the terminology, begin with Tensor API fundamentals and the tokens, embeddings and tensors article. If you already have an implementation, start with the boundary that is causing difficulty: a prompt contract, an inference endpoint, a classification policy or a usage record.
Independent coverage and corrections.
Provider names identify the systems discussed. TensorAPI.com is not affiliated with or endorsed by Anthropic, Microsoft Azure or Cursor. The guides do not imply a shared provider interface or a TensorAPI.com hosted inference service. Documentation is the source of truth for a provider’s current contract, and integration details should be checked against the configuration you use.
Corrections are part of useful technical writing. If you find an unclear example, a documentation change or a claim that needs a better source, send the page URL and the relevant detail to [email protected]. Please omit credentials, private datasets and confidential logs. Suggestions for a concrete new question are welcome through the same address.