3 posts
Document preparation experience repeats one lesson: time is lost not in the model but in the documents. A field note on scans, old versions, access and missing metadata.
Enterprise AI transformation experience shows the same patterns regardless of sector: data, ownership, pilot-to-scale and measurement. Field observations and early warnings.
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 Dirty or outdated documents, weak chunking strategies, poor metadata, missing retrieval evaluation, and underdesigned prompts can push even strong LLMs toward low-trust answers.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.