What AI QA covers
AI quality assurance applies the mindset of traditional QA to AI outputs. Where software QA asks does the code do what it should, AI QA asks does the AI say what it should: is it correct, on-brand, complete, and free of risky claims?
Because AI outputs are non-deterministic, QA cannot be a one-time test. The same prompt can produce different answers, and model updates change behavior. AI QA is therefore continuous: you check outputs on an ongoing basis, not just before launch.
How to do AI QA on automations
Define what good looks like as rules, check every (or a sample of every) output against them, route failures to alerts or human review, and track quality metrics over time. The goal is to catch a degradation in days, from your dashboard, rather than in months, from customer complaints.
Put AI quality assurance into practice with Tracira
Tracira adds output monitoring, plain-English guardrails, and human approval to your Make and n8n automations. One webhook, no code, free to start.
