You've trained an AI model to screen articles or extract data. The initial results look promising, but a nagging doubt remains: can you really trust it for your research? The leap from demo to defensible methodology is where quality control becomes non-negotiable. The Core Principle: Validation is a Multi-Layer Process Trust is built through systematic validation, not hope. Moving from a single accuracy score to a structured, multi-layered framework ensures your AI's output is research-ready. This process transforms the AI from an opaque black box into a validated, auditable component of your scholarly workflow. A Practical Framework: Pre, During, and Post-Validation Think of validation in three phases. Pre-Validation sets the standard: create a manually vetted "gold-standard" dataset of at least 50 studies and define performance benchmarks (e.g., Recall > 0.95 for screening). During Validation , you run your AI pipeline on this gold standard, calculate formal metrics, and diagnose failures.…