A Lightweight Evaluation Suite for AI UI Regressions
Explore how to create a lightweight evaluation suite that identifies AI UI regressions by testing invariant UI behaviors rather than exact text. Learn to use golden prompts, schema validation, snapshots, and tool-call assertions to ensure consistent UI states, catch structured output drift, and prevent duplicate tool executions. This lesson equips you to maintain reliable AI UI features with concise, effective regression tests.
A copy tweak or a schema rename often looks harmless in a pull request, but AI UI features can break in quieter ways. A field changes from summary to overview, the model still returns valid JSON, and the renderer shows a blank card because the mapping code reads the old key. Nothing crashes. The regression ships.
A lightweight evaluation suite targets invariants that stay true even when wording varies.
Test invariants, not exact phrasing
With non-deterministic generation, exact text matching fails for the wrong reasons, but UI behavior still has stable checkpoints. An evaluation can assert that the UI reaches a terminal state, renders required sections, and rejects unsafe HTML without caring about synonyms in the body text.
Common invariants for AI UI regressions include:
Schema validity for structured outputs that feed components.
Tool call shape such as tool name and argument parsing.
Latency budgets such as failing if p95 exceeds a threshold in CI ...