Acceptance‑Test‑First Mini‑Features: Templates & Examples to Ship Trustworthy AI Microfeatures in 48 Hours

AI microfeatures (small, single-purpose LLM-powered interactions — e.g., summarizer, intent classifier, safe reply generator) are high‑leverage but brittle. Writing acceptance tests first forces clarity: what the feature must do, what it must never do, and concrete input/output examples the contractor can implement against.

Acceptance-test-first aligns product, design, and engineering: the test becomes the contract. For AI features, that contract must include both functional assertions (UI behavior, API responses) and semantic assertions (output safety, hallucination guards). This reduces rework and prevents common issues where tests only check superficial success signals like 'toast shown'.

Scope tightly: pick a single happy‑path outcome, 3 negative cases, and a safety rule. Break work into three checkpoints: (A) acceptance tests + fixtures written and reviewed, (B) implementation that calls the model behind a testable adapter, (C) integration and CI gating. With Playwright and small API stubs you can validate UIs and end‑to‑end assertions within a day.

Use LLMs to bootstrap tests, but treat their output as draft code. Prompted test generation accelerates scaffolding (locators, steps), but teams must lock selectors (data-testids), add robust assertions, and add semantic validators before merging.

Below are six compact test templates you can copy into your test suite. Each example pairs: (1) a Playwright test that exercises the UI flow and asserts structural output; (2) a short LLM prompt pattern used to generate or validate the model output; (3) a JSON fixture with input examples and expected constraints. The goal is a developer can run npx playwright test and see pass/fail immediately.

The examples intentionally focus on observable invariants and safety checks, not internal model logits. For LLM assertions we use deterministic validators: schema checks, forbidden‑token lists, length limits, and example‑based similarity thresholds rather than fuzzy subjective checks.

Use Playwright for the UI/flow acceptance tests and keep the LLM interactions separated behind a small adapter you can stub in tests. Several community tools and packages show patterns for embedding LLM calls into Playwright tests or scaffolding tests with AI — but always require a human‑reviewed prompt and fixture set before trusting generated assertions.

Prompt patterns: (A) instruct the model as a constrained extractor with explicit output schema; (B) include 3 positive and 3 negative examples (few‑shot) in the prompt for determinism; (C) append a strict 'Do not invent' rule and an 'If unsure, respond with: I DON'T KNOW' fallback. These patterns reduce hallucinations and make outputs testable.

Require each contractor to complete this acceptance-test-first checklist before they implement model code: tests checked in, fixtures included, model adapter stubbed, and safety rules encoded. This guarantees you have an executable spec that reviewers and CI can run without live model access.

Make the checklist part of the PR template and the hiring/contractor onboarding. It converts vague requirements into binary gate checks: either the tests exist and fail (expected), or they do not — which prevents premature implementation and expensive rollbacks.