How to Test AI Features in Your SaaS: The 2026 LLM Evals Playbook
Muhammad Talha · Founder & Lead Engineer, Devs & Logics · Updated September 2026
Quick takeaways
- You can't unit-test a probabilistic feature, but you can eval it: run every change against a versioned set of 50–100 real cases and track pass rates like test coverage.
- Pick metrics by feature type — RAGAS-style metrics for RAG, exact match for extraction, calibrated LLM-as-a-judge for open generation, task completion for agents.
- A calibrated LLM judge agrees with human reviewers about 85% of the time — but an uncalibrated one drifts: position bias, verbosity bias, and family bias all inflate scores.
- Four judge rules: binary criterion-separated rubrics, pairwise with order swapping, a judge from a different model family than the generator, and monthly calibration against human labels.
- Close the loop: production failures become new golden-set cases, and the suite runs in CI so a prompt edit can't silently break three things while fixing one.
Traditional tests have deterministic expected outputs; AI features don't — the same input can produce several different, equally valid responses, so a conventional assertion either fails constantly or tests nothing. That doesn't mean you can't test AI features. It means the test changes shape: instead of asserting one exact output, you run an LLM eval — a versioned suite of real cases scored against criteria — and treat the pass rate the way you treat code coverage. This is the playbook we use in our AI integration services: four layers, from deterministic unit tests to production monitoring, with the eval suite doing the work conventional tests can't.
The 2026 AI Testing Pyramid
Four layers, cheapest and fastest at the bottom: 1. Deterministic unit tests cover the code around the model. 2. The eval suite covers the model's behavior. 3. End-to-end tests cover the user experience with the model mocked out. 4. Online evals watch production, feeding what they catch back into the suite.
Most teams that ship unreliable AI features have layers one and three and are missing the two that actually measure the AI.
Layer 1: Deterministic Unit Tests for the Code Around the Model
Everything that wraps your AI calls is still normal TypeScript and gets tested the normal way with Vitest: prompt construction, output parsers, schema validation, cost calculators, rate limiters, retry logic. Never call a real LLM here — mock the client and assert on your handling of responses, errors, and timeouts.
describe('Prompt Builder', () => {
it('includes user context in the system prompt', () => {
const prompt = buildSystemPrompt({ userPlan: 'pro', language: 'en' });
expect(prompt).toContain('pro plan');
expect(prompt).toContain('English');
});
});