The First QA Checklist I Would Run On Any AI-Built App In 2026

2026-06-20 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Novice, long

Summary

This article outlines a 10-point Quality Assurance (QA) checklist specifically designed for applications built with AI coding tools, aiming to ensure functionality beyond superficial appearances. It highlights the danger of assuming an AI-built app is "done" just because it looks complete, stressing the importance of proving it works under various conditions. The checklist includes defining a single user workflow, executing the "happy path" slowly, frequently refreshing to confirm data persistence, rigorously testing empty and invalid inputs, verifying empty states for new users, checking account boundaries with at least two test accounts, requiring AI to provide a test plan before implementing bug fixes, performing regression tests after every fix, and evaluating loading and error states. The author also offers a free AI App Builder Starter Prompts pack for beginners.

Key takeaway

For AI Engineers or Software Engineers building applications with AI tools, prioritize a structured QA approach over superficial checks. Your AI-built app may appear functional, but critical issues like data persistence, input validation, and account security often hide beneath the surface. Implement a workflow-centric checklist, including frequent refreshes and multi-account testing, and always demand a test plan from AI before applying fixes to prevent new regressions. This ensures your app truly works, not just "looks done."

Key insights

AI-built apps require rigorous workflow-based QA, as visual completeness often masks underlying functional flaws and data persistence issues.

Principles

• Test the workflow, not just the visible parts.
• Specific expected behavior beats vague checks.
• Define "done" clearly for testable outcomes.

Method

The proposed QA method involves defining a single user workflow, executing the "happy path" slowly, refreshing frequently, testing edge cases like empty/invalid inputs and empty states, verifying account boundaries, and demanding AI-generated test plans for bug fixes.

In practice

• Use two test accounts for boundary checks.
• Refresh after every create, edit, delete action.
• Ask AI for a test plan before fixing bugs.

Topics

• AI-Built Applications
• Software Quality Assurance
• Workflow Testing
• Regression Testing
• Data Persistence
• Input Validation

Best for: AI Engineer, Software Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.