Excellent article. One thing we've consistently seen in enterprise AI deployments is that RAG isn't a silver bullet. It significantly improves grounding, but its effectiveness still depends on retrieval quality, data freshness, chunking strategy, and continuous evaluation.

As AI systems become more agentic, it's equally important to know when retrieval should be used, when additional reasoning is needed, and how to monitor these systems in production. Understanding these limitations early helps teams build more reliable and trustworthy AI applications.

We recently shared our perspective on this topic as well: mlaidigital.com/blogs/where-rag-fails-understandi…. Curious to hear your thoughts on where you think the industry is heading beyond traditional RAG.