7 Quality Checks to Automate Before Your AI App Ships to Production
goyalshaliniuk · x · 2026-10-09
The author notes AI apps often pass testing perfectly yet fail in production; instead of manually inspecting every response, automate 7 quality checks: output format validation (JSON structure, required fields, types), factual accuracy (citation checks, contradiction detection), relevance & instruction following, hallucination detection (compare answers against retrieved docs, flag unsupported claims and invented references), safety & privacy scanning (PII exposure, API key leakage, policy violations), performance & latency tracking (response latency, token usage, cost per request, timeout rates with threshold alerts), and regression testing (run a representative suite after every change, compare against previous versions, block releases on critical failures). Core message: every AI update should prove it hasn't broken what already worked.
Related event: Seven Automated Quality Checks Every AI App Needs Before Launch(3 posts)→
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