3 posts
RAG evaluation is a methodology that measures retrieval quality (recall@k, MRR, nDCG) and generation quality (faithfulness, answer relevance) separately. A layer-by-layer guide.
Evaluating large language models in enterprise environments cannot be limited to benchmark scores or impressive demos. In production, the real question is not how intelligent a model appears, but how accurate, safe, cost-sustainable, and controllable it is. Accuracy alone is not enough; safety, compliance, human review, guardrails, latency, total cost of ownership, auditability, and behavioral consistency must all be considered together. This guide explains how enterprises should structure LLM evaluation across four core dimensions—accuracy, safety, cost, and control—using systematic eval design, test sets, risk classification, operational metrics, and governance principles.
RAG projects often look impressive in demos but begin to fail in production due to quality, trust, and sustainability problems. In most cases, the root cause is not the model itself, but structural weaknesses in data preparation, retrieval design, evaluation discipline, and prompt behavior. Dirty or outdated documents, weak chunking strategies, poor metadata, missing retrieval evaluation, and underdesigned prompts can push even strong LLMs toward low-trust answers. This guide explains why RAG projects fail and provides a production-oriented framework for building more reliable systems across data preparation, evaluation, and prompt design.