AI-assisted API development works best when the coding environment receives a precise task and the evidence needed to complete it. A repository can contain thousands of files, yet a small change may depend on one route, one schema, one adapter and a few tests. Deliberate context helps a coding assistant reason about those relationships without asking it to guess which conventions are authoritative.

Cursor is an AI code editor that can support this development process. It is separate from the model endpoint your application calls. In this guide, “Cursor Tensor API” means using the editor to build an API workflow; it does not describe a general public tensor inference service. The Cursor workflow overview introduces that distinction and the role of a local development environment.

Begin with a verifiable outcome

Describe the behavior that should change before requesting code. For example, an inference adapter may currently retry every error, including malformed input. The desired outcome is that it rejects invalid tensors immediately, retries only selected transient failures and reports a stable error object. That description gives the assistant a concrete responsibility and gives the reviewer a way to judge the result.

Add a small example of accepted input, an example of rejected input and the expected response for each. Explain whether the change affects a public contract or only an internal helper. If the repository already contains a schema or design decision, identify it as the source of truth. A paragraph of explicit requirements can be more useful than a long conversation about abstract code quality.

Select context by dependency

Gather the files that explain the execution path. For an API change, this often includes the route handler, request validator, provider adapter, response type and existing tests. Include the package configuration when dependency versions affect the implementation. Add the caller when it relies on a behavior that the server code alone does not reveal.

Do not assume that a filename explains a component’s responsibility. Ask the assistant to trace how an input reaches the model and how errors return to the caller. Review that trace against the code before approving a broad edit. If the explanation cannot identify the actual integration boundary, supply missing context or narrow the task.

Keep large generated files, unrelated examples and old experimental implementations out of the focused context unless they are necessary. Their presence can make an obsolete pattern look like an established convention. Context selection is a form of technical editing: include evidence that resolves the current decision.

Use project rules for durable knowledge

The official Cursor rules documentation describes project rules stored in .cursor/rules, with scope and application behavior that control when their contents enter model context. Rules can preserve repository conventions and repeated instructions. Keep them concise enough that a contributor can inspect the guidance and understand its purpose.

A useful API rule states the public error envelope, where schemas live, how tests run and which module owns provider authentication. It should point to maintained examples instead of copying a large implementation. Avoid turning a rule into a history of every previous mistake; that makes conflicting or outdated instructions harder to notice.

For example, a team might record this original project guidance:

Validate public requests with the shared inference schema.
Keep provider credentials in the server adapter.
Return documented error codes from route handlers.
Use the existing adapter fixtures for transport behavior.
Report any public contract change in the change description.

These rules communicate development expectations. They do not replace compiler checks, runtime validation, credential permissions or code review. Enforce important boundaries in the application and repository tooling as well.

Give the assistant a bounded implementation request

A productive request names the goal, relevant files and acceptance conditions. Consider this example: update the tensor request validator so every row has the declared feature count; reject non-finite numeric values; preserve the current response schema; add examples for a ragged array and a valid batch. The assistant can now connect input constraints to observable outcomes.

Ask for a short explanation of the proposed approach before a complex edit, especially when several modules may be affected. For a routine change with a clear contract, proceed directly to implementation and inspect the diff. The amount of planning should reflect uncertainty, not a fixed ritual applied to every task.

Keep unrelated refactoring outside the requested change. If the assistant discovers a broader design problem, record it and decide whether it is necessary to finish the current behavior. This preserves a reviewable connection between the problem, the code change and the evidence that it works.

Keep provider details at the adapter boundary

A model provider may use its own request fields, response blocks and error categories. Give the coding assistant the relevant official interface documentation and the installed client version. Ask it to translate that contract into the application’s internal types. Avoid allowing provider-specific objects to spread through unrelated routes and user interface components.

When a project handles numerical arrays, document the shape and data type at this boundary. A list accepted by a JSON parser is not necessarily valid model input. The adapter should know whether it sends raw text, embeddings or a serialized tensor, and it should make incompatible shapes explicit. The Tensor API fundamentals guide describes the fields that make these contracts understandable.

For a language API, preserve meaningful response metadata separately from generated content. Do not let a convenient text extraction helper silently discard an incomplete response indicator that the caller needs to assess success.

Use fixtures to exercise failure behavior

Provide small, deterministic fixtures for transport and parsing tests. Include a normal response, a rejected request, a timeout and a response with an unexpected structure. Tests should verify the application’s behavior for those cases without requiring a live model call on every run. This makes iteration faster and avoids confusing provider variability with a code regression.

Live integration checks still serve a purpose when a change depends on the actual provider contract. Keep them bounded, use an appropriate test environment and state which behavior they verified. Passing a mocked test does not prove that credentials, networking or the selected provider parameters work in deployment.

For model quality, use reviewed evaluation examples rather than ordinary unit assertions alone. The guide to classification confidence and evaluation explains why correct parsing and useful predictions require different evidence.

Review the diff as a maintainer

Read the changed code with the original problem beside it. Check whether the implementation validates the right boundary, preserves expected behavior and handles the stated failure cases. Inspect new dependencies, configuration changes and side effects. A cleanly formatted diff can still introduce a hidden contract change.

Ask the assistant to explain one representative request from entry to response using the finished code. This is especially helpful when a change spans validation and error handling. Confirm that the explanation matches the implementation instead of accepting a plausible description detached from the files.

Use automated checks where they establish something useful: type checking for incompatible interfaces, targeted tests for changed behavior and a build when packaging may be affected. Repeating broad test runs after a documentation-only correction usually adds less evidence than reading the corrected text.

Leave a compact handoff

When the change is complete, record the behavior that changed, the reason, the verification performed and any unresolved limitation. Include a migration note if callers must send a new field or handle a new error. Update durable project rules only when the work established a reusable convention; temporary task details belong with the change itself.

Clear handoffs also improve the next AI-assisted session. Future context can point to the maintained contract and focused examples, rather than reconstructing decisions from a long chat transcript. The repository should remain understandable to someone who never saw the original conversation.

Conclusion: context is part of the engineering work

Using Cursor well involves selecting authoritative files, stating a testable outcome and reviewing the resulting implementation. Project rules preserve repeatable knowledge, while schemas, tests and permissions enforce actual behavior. Keep the development editor distinct from the model API, make tensor contracts explicit and finish with evidence tied to the changed code. The result is an API workflow that a human maintainer can understand and continue confidently.