Why LLM Apps Need Inline Validation Built In From Day One

A developer building an AI-powered app called Slooster learned the hard way that traditional software testing methods fail when large language models are involved, as LLMs can produce inconsistent or nonsensical outputs even from identical prompts. During a staging demo, prompts that had reliably returned real vendor data for weeks suddenly produced placeholder text like 'Vendor A' and 'Vendor B.' To address this, the developer built an inline validation layer with automatic retries that catches bad outputs before users see them. The system uses two layers of checks: a structural schema validator and a secondary AI prompt that evaluates whether the output is semantically meaningful and meets the original criteria. The key lesson is that validation must be continuous, configurable, and built into the application from the start, not treated as an afterthought.