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AI Tests and Docs: A Contract-First Guide for 2026

Using AI to write tests and documentation should start with a clear rule: generate drafts, then verify behavior. For your 2026 workflow, keep requirements authoritative and execution measurable. GitHub’s responsible-use guidance similarly emphasizes reviewing and validating AI-generated output rather than treating it as automatically correct. ([docs.github.com](https://docs.github.com/en/copilot/responsible-use/code-review))

1. Give AI a Contract, Not Just Source Code

Before requesting tests, assemble a small context packet: the target function, its public interface, relevant requirements, dependency versions, and one existing test that demonstrates repository conventions. Include intended errors and boundary behavior. Keep unrelated files out.

Separate intended behavior from observed behavior. Ask the model to flag disagreements between requirements and implementation instead of silently choosing one. For legacy code without a specification, label generated tests as characterization tests until a maintainer confirms the behavior is desirable.

2. Request a Test Matrix Before Test Code

Use a two-stage prompt: first review scenarios, then generate the approved tests. For a hypothetical pagination function whose contract requires positive page sizes, request:

• Normal cases: a full page and a partially filled final page.
• Boundaries: an empty collection, page size one, and an exact page boundary.
• Invalid inputs: zero or negative page sizes, with the contract’s specified exception.
• Invariants: concatenating all pages preserves item order and loses no items.

Reusable prompt: “Create a scenario matrix from this contract. For each case, state inputs, expected results, and the requirement it verifies. Identify ambiguities. After approval, write tests using our existing fixtures. Do not modify production code or derive expected values by calling the function under test.”

For Python projects, request pytest parametrization for related input-output cases; its parametrization feature runs a test against multiple argument sets. ([docs.pytest.org](https://docs.pytest.org/en/stable/how-to/parametrize.html))

3. Verify That Tests Detect Wrong Behavior

Review imports and fixtures, then run the focused tests locally. Reject assertions that merely check a result exists when the contract specifies its contents. Mock external boundaries, such as network responses, without mocking away the behavior being tested. Use synthetic data and controlled clocks where appropriate.

With coverage.py installed, run coverage run --branch -m pytest, followed by coverage report -m. Branch coverage identifies untaken execution paths that statement coverage can miss; use those gaps to guide additional scenarios, not as proof of correctness. ([coverage.readthedocs.io](https://coverage.readthedocs.io/en/latest/branch.html))

Then deliberately introduce a small defect in a disposable branch, such as dropping the final page. Confirm the relevant test fails, restore the implementation, and rerun. Never let the assistant weaken assertions simply to obtain green results.

4. Write Documentation From Verified Evidence

Once behavior is approved, provide the model with the public interface, accepted requirements, and passing examples. Request documentation for a specific audience: a quickstart for new users, an API reference for integrators, or a migration guide for existing customers.

• Document parameter types, defaults, return values, exceptions, and side effects.
• Include one minimal success example and one actionable failure example.
• Explain constraints and trade-offs rather than narrating every implementation line.
• Mark unknowns for review; never invent flags, endpoints, or guarantees.

Make examples executable. For Sphinx projects, enable sphinx.ext.doctest and run its doctest builder in CI; the extension executes appropriately marked documentation snippets and checks their output. ([sphinx-doc.org](https://www.sphinx-doc.org/en/master/usage/extensions/doctest.html))

5. Keep Iteration Affordable With 59API

59API is a practical low-cost relay for repeated drafting and review. It offers pay-as-you-go token billing, supports Claude Code, Codex, Gemini CLI, and OpenAI-compatible SDK workflows, and advertises full model capability. Its referral rebate is an additional benefit, separate from usage pricing. ([59api.com](https://59api.com/))

For a small evaluation, compare Claude Haiku 4.5 and Claude Sonnet 5.5, both listed in 59API’s catalog. Use the same task and measure accepted scenarios, correction time, and total request cost—not output length. ([59api.com](https://59api.com/pricing))

6. Make Review the Release Gate

Require focused tests, the full relevant suite, documentation checks, and human approval before merging. Remove secrets before sending context, review provider and relay data policies, and limit agent permissions. Keep generation outside mandatory CI so merges do not depend on a fresh model response.

If you want to pilot this workflow economically, sign up for 59API and start with one small module. Your success metric is fewer missed behaviors and clearer runnable documentation—not more generated files.

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