Why 80% of RAG projects fail in production
It's not a model problem. It's an architecture problem, a chunking problem, and a people-confusing-a-POC-with-a-system problem.
Your RAG answers all 10 test questions correctly. The room applauds. You schedule the rollout. Six months later, users are complaining about nonsensical responses, the IT team quietly stopped monitoring anything, and your internal champion has taken a new role. Congratulations — welcome to the 80%.
What's striking is that everyone knows this is coming. Data teams have watched this exact film play out with classical machine learning. But something about the magic of LLMs makes people believe it's different this time. It isn't. It's worse — because errors are harder to detect, and users trust a fluent prose answer far more than a number in a table.
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