The RAG Plateau: Why Vector Search Is Failing the Enterprise
In the early days of generative AI, retrieval-augmented generation (RAG) was a revelation. By grounding large language models (LLMs) in external data, we solved the immediate problem of static knowledge. However, as we move through 2026, enterprise developers have hit what I call the “RAG Plateau.”
Standard RAG relies on vector databases and cosine similarity. This works perfectly for “flat” queries—where the answer exists within a single paragraph of text. But enterprise data isn’t flat; it’s a web of interconnected dependencies. If you ask an AI, “Which microservices are at risk if the ‘User-Auth’ database experiences 500ms latency?”, a vector search will find snippets about “User-Auth” and “Latency.” It will almost certainly fail to map the three-hop relationship between the database, the authentication service, and the downstream billing gateway.