Stop Vibe-Checking Your AI Product: A Systematic Approach to Finding and Fixing Failures
Replace subjective AI quality checks with a repeatable workflow for finding, classifying, and fixing product failures.
Outcome
A clearer failure taxonomy and repeatable review loop for improving AI product behavior.
Workflow steps
Define failure classes
Separate accuracy, relevance, latency, refusal, tone, safety, and user trust failures.
Sample real cases
Review real user inputs and outputs instead of relying on cherry-picked demos.
Prioritize fixes
Rank failures by user harm, frequency, and fixability.
Why this workflow matters
AI product quality is easy to judge by vibes and hard to improve that way. This workflow creates a structured way to identify where an AI feature fails and what to fix first.
How to run it
Collect real examples, classify each failure, identify the likely cause, and decide whether the fix belongs in prompting, retrieval, UI constraints, model choice, or product policy.
What good looks like
Your team should move from vague complaints to a prioritized backlog of specific AI product improvements.