The developer’s tensor field guide
Make sense of the data behind AI. Explore tensors, LLM workflows, prompts and classification—with practical guides that connect the pieces.
Three axes, twenty-four values: two matrices, each with three rows and four features. The stack is a schematic.
01 / Find your starting point
From the first shape to a thoughtful model workflow. Start with the question you are trying to answer.
Understand shapes, types and contracts before the data crosses an API.
Explore Tensor API 02 / THE MODEL LAYERConnect prompts, tokens and model outputs to a usable application workflow.
Explore LLM workflows 03 / THE DECISION LAYERDesign labels, validation and evaluation around the decision you need to make.
Explore classification02 / Look inside the contract
A useful interface makes the data’s meaning explicit. Start with a small example, name the dimensions, and define what the next step can rely on.
Give each field a purpose, a type and a permitted shape.
Choose fields and labels that the caller can inspect.
Make uncertainty and invalid output part of the workflow.
TensorAPI.com{
"input": {
"name": "features",
"dtype": "float32",
"shape": [
2,
3
],
"values": [
[
0.2,
0.8,
0.4
],
[
0.5,
0.1,
0.9
]
]
},
"contract_version": "example-1"
}03 / Map the workflow
Understand each tool’s role before you connect the pieces.
Understand the message boundary, structured responses and where tensor processing belongs in your application.
AzureInference & deploymentSeparate the endpoint, model deployment and preprocessing contract before you choose an inference path.
CursorDevelopment & contextUse repository context and clear requirements to build, inspect and refine an API implementation.
04 / Tensor API Lab
Original field notes for the questions that come up when you start connecting AI systems.
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 readTrace text from vocabulary tokens to embedding vectors and shaped tensors. Understand how masks, pooling and model versions affect API design, memory estimates and reliable similarity search. Keep representation size separate from the meaning of its values.
7 min readMake LLM consumption predictable with a budget for each task. Separate context limits from output reserves, choose batching and cache policies, and connect estimates, retries, and reported usage to useful completion.
7 min readUse Cursor to develop inference integrations with focused files, durable project rules and testable tasks. Learn how adapter boundaries, realistic fixtures and human review turn generated changes into maintainable code. Finish with a clear implementation handoff.
7 min readConnect prompt design to an explicit output contract. Use schemas, unavailable states, source-reference checks, bounded corrections, and focused evaluation cases to turn generated text into records that an application can validate.
7 min read05 / A few good questions
Get oriented, then follow the guide that matches your next decision.
A tensor API is an interface for creating, transforming or exchanging numerical arrays with a defined shape and data type. In a model-serving context, it can also describe the contract for a model’s inputs and outputs. Start with our Tensor API guide.
They describe different parts of a workflow. Tokenization maps input into model-specific units; embeddings represent items numerically; tensors arrange numerical data along axes. Read the tokens, embeddings and tensors explainer for a concrete walkthrough.
Their roles differ. Anthropic provides hosted model interfaces, Azure supports several model and deployment workflows, and Cursor supports software development. Our integration guides explain each boundary and point to the relevant official documentation.
Define the labels, what evidence supports them, and what should happen when an input is ambiguous. Then decide how you will evaluate the result. The classification guide connects those decisions to an API contract.
Browse the Tensor API Lab or add the RSS feed to your feed reader. For a correction or a topic suggestion, email [email protected].
