LLM Best Practices

Testing

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This is LLM Best Practices (llmbestpractices.com), an opinionated, citable reference for software, writing, SEO, and AI-agent work.

  1. Route, do not crawl. Fetch llmbestpractices.com/llms.txt and open only the pages whose one-line summary matches your task.
  2. Read raw. Append .md to any page URL for markdown. Check status and last_updated in the frontmatter, then read the rules.
  3. Apply as defaults. First-party docs and the project's own conventions win on conflict. Warn before relying on a fast-moving page older than 12 months.
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Overview

A test suite earns its cost by catching the regression you would have shipped and by documenting intended behavior. This page is the language-agnostic policy; framework mechanics live in python-testing and swift-testing. Read general-principles first.

Weight the suite like a trophy, not a pyramid

Put most effort in the middle:

  • Static checks at the base: types, lint, schema validation.
  • A thick integration layer: the function under test plus a real database or HTTP server.
  • A few golden-path end-to-end tests: one login, one checkout.
  • Unit tests reserved for tricky pure logic: a parser, a pricing rule, a date calculation.

Unit tests of glue code mostly test the doubles. A Postgres instance in a container catches bugs a mocked repository hides. See fastapi for the HTTP layer and observability for telemetry.

Treat every flaky test as a defect

A flaky test teaches the team to ignore red builds, and the real failure hides in the noise. Quarantine it on the first observed failure, file a ticket with the seed and stack trace, then land a deterministic version within a sprint or delete the test.

Make tests deterministic

Wall-clock time, network availability, and unseeded randomness will eventually fail for reasons unrelated to your code.

  • Freeze time with a clock parameter or a library (time-machine, freezegun, vi.useFakeTimers).
  • Stub external HTTP (respx, msw, nock) or replay recorded cassettes.
  • Seed every random source; pass the generator into the function under test.
  • Run tests in random order locally to expose hidden ordering coupling.

Name tests for the one rule they enforce

test_total_excludes_voided_line_items tells the reader what broke; test_calculate_total_1 does not. The failing name should make the offending diff obvious. Each test asserts one rule; several assert lines are fine when they express the same rule, and a test that checks five unrelated things should be split.

Use snapshots only for stable formats

A passing snapshot proves the output did not change, not that it is correct, and the first run freezes whatever bug existed. Snapshot stable serializations (a JSON-LD blob, a compiler IR) where a human reviews every diff. Avoid them for component trees and formatted strings that developers will update without reading.

Prefer fakes and real dependencies to mocks

A test that mocks five collaborators is testing the mocks, and the function under test probably does too much. Use an in-memory fake (FakeUserRepo) over a Mock that asserts call counts, and a real dependency in a container over any double. Use call-assertion mocks only for side effects with no observable result, such as an email send.

Set a coverage floor, never a target

Coverage shows that critical paths run at all, not that they are checked. Pick a floor your suite already clears and fail CI below it; do not gate a PR on coverage rising. The real bar is the four cases in general-principles.

Test the public contract

Test what a user, integrator, or upstream caller would notice. Tests of private helpers churn with every refactor; tests of the contract survive it. For LLM-facing systems, write evals at the contract layer (prompt input, JSON output, citations), not at every internal call. See evaluation and rag.

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