Most RAG systems fail at retrieval, not generation. The language model can only be as good as the documents you give it. Learn how chunking strategy, hybrid search, and proper evaluation determine whether your RAG system works in production or joins the 70% that fail.
RAG implementations cost 2-3x initial estimates. Infrastructure expenses, development overhead, and operational costs nobody mentions in sales demos. Vector databases, embedding APIs, development time, and ongoing optimization add up quickly. Learn what teams consistently underestimate and how to budget accurately from day one.
Five good knowledge sources often outperform two excellent ones. Multi-source RAG systems succeed through diversity, not individual quality. Integrate multiple sources and build systems users trust.
Automated RAG evaluation metrics do not predict which systems people trust and use daily. Precision scores and faithfulness ratings miss what matters - user behavior and task completion. Here is how to build evaluation frameworks that measure real success in production AI systems.
A RAG system that is 85% accurate but easy to use will beat one that is 95% accurate but frustrating. Here is how to design AI systems that non-technical users actually adopt.
The choice between RAG and fine-tuning is not about which is better. It is about data freshness, team capacity, and whether your knowledge changes daily or yearly.
RAG systems dont create new security risks - they amplify whatever data security posture you already have. Weak access controls become glaringly obvious when your AI can retrieve anything.