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Clear request and response contracts make AI systems easier to operate.
These articles focus on the boundary between a caller and a capability: field names, input meaning, validation, versioning and failure behavior. Start here when a successful request can still produce an unusable result, or when two clients interpret the same field differently.
Keep a small example of an accepted request and a rejected request beside the documentation. For each response field, ask who consumes it and what action depends on it. The collection approaches API design from tensor, classification and provider-workflow perspectives, helping you separate a stable application contract from the implementation behind it.
4 articles tagged API design
Use 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 readChoose the right Azure endpoint for an inference workload, define numerical data contracts and keep preprocessing, model versions, batch recovery and rollout decisions traceable across the full pipeline. Cover both interactive requests and larger asynchronous classification jobs.
7 min readTurn classifier outputs into decisions that can be evaluated. Define label semantics, distinguish scores from calibrated probabilities, choose review thresholds, and measure precision, recall, coverage, and failures across realistic inputs.
7 min readA tensor contract needs more than an array of numbers. Learn how to name axes, validate numerical types, preserve feature meaning, and batch compatible examples without hiding errors at the API boundary.
7 min read
TensorAPI.com
