Most people think dense vector search works like this: embed your documents store the vectors run cosine similarity Done. This is the biggest misunderstanding in modern AI systems. Dense vector search looks simple, but in real deployments it becomes one of the hardest layers to scale—and often the true bottleneck behind: slow RAG pipelines inconsistent…
A systems-level explanation for engineers, architects, and anyone building RAG, search, or agent infrastructure. Dense retrieval looks clean on paper. You take an embedding model, generate vectors, drop them into a vector database, and let an ANN index handle the rest. But once you go beyond a single machine, dense search becomes something very different:…
Most AI failures don’t happen inside the model. They happen one layer earlier — in retrieval. If your RAG system, copilot, or agentic workflow is hallucinating, the LLM probably isn’t “dumb.” It’s operating on bad context. Dense search alone can’t fix that. Sparse search alone can’t fix that. Hybrid retrieval — the combination of semantic…