Fordel StudiosTest infrastructure that keeps pace with Cursor-speed development.
AI-assisted development increases commit frequency without proportionally increasing test coverage. The result is a flaky test suite that slows the team down rather than catching regressions. We fix the underlying problem: test architecture designed for change tolerance, LLM-assisted generation for the boilerplate, and semantic assertions that check intent rather than pixel coordinates. The output is a test suite your team trusts.
Teams shipping with Cursor and Copilot report a consistent pattern: code output increases, test coverage does not. The tools generate the implementation but rarely generate the tests that validate it. When tests do get generated, they often test the implementation rather than the specification — a pattern called confirmation bias testing that looks like coverage but provides no safety net against incorrect behavior.
There is a second problem specific to AI features: non-deterministic LLM outputs cannot be validated with exact-match assertions. A customer support agent that was working last week and was subtly degraded by a model update or a prompt change will not fail a unit test. It will just start giving slightly worse answers until someone notices. Without an eval harness — a dataset of representative inputs with scoring criteria — you have no signal on whether your LLM features are regressing.
| QA challenge | Traditional approach | AI-augmented approach |
|---|---|---|
| Test generation speed | Manual — developer writes every test | LLM generates scaffolding from types and signatures; developer reviews |
| E2E selector maintenance | Manual updates every UI change | Playwright semantic selectors plus self-healing on DOM changes |
| Visual regression | Manual screenshot comparison | Chromatic or Percy with AI-assisted noise filtering |
| LLM output quality | No standard mechanism | LangSmith eval datasets with LLM-as-judge scoring |
| Flaky tests | Manual investigation per flake | Retry analysis and selector stability scoring to surface root cause |
We build test infrastructure at all three layers of the testing pyramid. Fast unit tests with real coverage at the base — generated scaffolding plus human-reviewed assertions. Integration tests for service boundaries and external API behavior. A focused Playwright E2E suite for critical user journeys, using ARIA roles and data-testid selectors rather than CSS paths that break on every redesign.
For AI features, the eval harness is the most important investment. We build LangSmith evaluation datasets with representative inputs and scoring criteria: rule-based checks for structured outputs, semantic similarity for prose, and LLM-as-judge for subjective quality dimensions. Aggregate scores are tracked over time. A model update or prompt change that degrades performance shows up in the eval run before it reaches users.
AI-powered QA layers we build
LLM generates test scaffolding from function signatures and types as part of the PR flow. Generated tests are flagged for human review on assertion logic. Coverage gaps are reported automatically. Shift-left: quality feedback in the PR, not post-merge.
Playwright tests written against semantic selectors — ARIA roles, labels, data-testid attributes — that survive CSS and layout changes. When selectors break, the system attempts automated repair. Critical path coverage: auth, core value delivery, payment flows.
Chromatic captures component and page screenshots on every PR. Pixel diffs are reviewed in the Chromatic UI with baseline management. Storybook integration means component-level visual regression runs alongside unit tests.
LangSmith evaluation datasets with representative inputs, expected behavior criteria, and automated scoring pipelines. LLM-as-judge scoring for subjective quality. Ground-truth comparison for structured outputs. Regression alerts when scores drop.
- 01
LLM-assisted test scaffolding
We integrate LLM-powered test generation into the PR workflow: scaffolding is generated from function signatures, TypeScript types, and existing test patterns. Developers review and complete assertion logic — the goal is to eliminate the setup cost, not offload the judgment. Generated tests go through the same human review gate as any other code.
- 02
Playwright E2E with semantic selectors
We write Playwright tests against ARIA roles, labels, and data-testid attributes rather than CSS class names or DOM structure. Tests written this way survive component redesigns and framework upgrades without selector rewrites. When selectors do break, self-healing tools like Playwright's built-in locator strategy and tools like Momentic reduce the maintenance cycle.
- 03
LangSmith evaluation harnesses
We build structured eval pipelines for AI features: ground-truth datasets, deterministic scoring for factual correctness, and LLM-as-judge scoring for subjective quality dimensions like tone and completeness. Aggregate pass rates are tracked over time so you see exactly when a model update or prompt change degrades output quality — before your users do.
- 04
Visual regression with Chromatic
Chromatic integrates with Storybook and Next.js to capture pixel-accurate visual snapshots on every PR. We configure noise filtering to suppress false positives from dynamic content and animations, and set up baseline approval workflows so regressions get flagged, not silently merged. Component-level and full-page coverage depending on your stack.
- 05
Shift-left quality gates
Quality checks run as close to the developer as possible: pre-commit linting via Husky, PR-level test runs with coverage delta reporting, and eval harness runs triggered by changes to AI-adjacent code paths. A bug caught in a PR costs minutes to fix; the same bug caught post-deploy costs hours plus the incident retrospective.
- Test coverage audit with gap analysis across unit, integration, and E2E layers
- LLM-assisted test scaffolding pipeline integrated into PR workflow with human review gates
- Playwright E2E suite for critical user journeys with semantic selectors
- Chromatic visual regression pipeline with noise-filtering and baseline management
- LangSmith eval harness with ground-truth datasets and automated scoring pipelines
- CI quality gates with coverage thresholds, flake detection, and regression blocking
Teams with structured Playwright E2E suites typically estimate 40–60% reduction in manual regression time after the first quarter of stable coverage. The bigger value is less visible: reduced fear of refactoring, faster code review cycles, and earlier regression detection on features that do not have QA owners.
Frequently
asked questions
How do you test non-deterministic LLM outputs?
Not with exact-match assertions. We build evaluation datasets with representative inputs and expected output characteristics — not exact strings. Scoring uses rule-based checks for structured fields, semantic similarity for prose, and LLM-as-judge for subjective quality. Aggregate pass rates over the dataset are tracked over time via LangSmith. A drop in aggregate score is the regression signal.
Do you work with existing test suites?
Yes. We audit existing coverage, identify high-value gaps, and extend rather than replace. Complete rewrites are rarely justified — the value is adding the AI-specific eval infrastructure that existing suites cannot provide, and filling coverage gaps in areas where the test/code ratio has degraded.
What is the flaky test problem and how do you address it?
Flaky tests — tests that pass and fail intermittently without code changes — are a signal of unstable selectors, timing dependencies, or test isolation failures. We address root causes: replace CSS selectors with semantic ones, add proper async waiting in Playwright, isolate test data between runs. Chromatic's flake detection and Playwright's retry analysis help surface which tests are flaky and why.
What is the right E2E coverage scope?
E2E tests are expensive to maintain. The target is not 100% coverage — it is coverage of the flows where a failure would be immediately visible to users and high-impact: authentication, core value delivery, and payment/subscription flows. Everything else is better covered at unit and integration layer.
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