Building a Full Evaluation and Guardrail System for a RAG App Publication-ready draft for Medium, dev.to, or a course blog. Summary In Lab 24, I built a full evaluation and guardrail layer around a retrieval-augmented generation system. The goal was not just to make a RAG demo work, but to make it measurable, safer, and easier to operate. The final system connects to my Day 18 corpus, generates an evaluation test set, runs RAGAS-style scoring, performs LLM-as-judge calibration, applies input and output guardrails, runs adversarial tests, benchmarks latency, and documents production SLOs in a blueprint. The system is intentionally reproducible. When API keys are unavailable, it uses deterministic fallback logic so every script still runs locally on Windows. Live Gemini judging, Groq output guarding, and Presidio NER are supported as opt-in extensions, but the default grading path remains stable. Day 18 Corpus Integration The evaluation set is grounded in the Day 18 RAG corpus.…