Category: RAG Technology

Every RAG system starts the same way: take your documents, split them into chunks, embed them, and retrieve them when needed. Simple, right? Except most teams are doing it wrong—and the cost is massive. While enterprises celebrate their latest language model upgrades, they’re quietly degrading retrieval quality through chunking mistakes that compound silently across millions…

Enterprise RAG systems are bleeding money. While organizations rush to implement AI-powered knowledge retrieval, they’re discovering that traditional RAG architectures scale about as gracefully as a freight train hitting a brick wall. Vector databases buckle under massive document loads, inference costs spiral into six-figure monthly bills, and performance degrades so badly that users abandon the…