When Embeddings Aren't Enough: A Decision Framework for Hybrid Retrieval Architecture
Most RAG implementations start the same way: spin up a vector database, embed documents with a decent model, run cosine similarity at query time, and ship it. The demo looks great. Relevance feels surprisingly good. Then you deploy it to production and discover that "Error 221" retrieves documents about "Error 222," that searching for a specific product SKU surfaces semantically similar but wrong items, and that adding a date filter causes retrieval quality to crater.
Vector search is a genuinely powerful tool. It's also not sufficient on its own for most production retrieval workloads. The teams winning with RAG in 2025 aren't choosing between dense embeddings and keyword search — they're using both, deliberately.
This is a decision framework for when hybrid retrieval is worth the added complexity, and how to build each layer without destroying your latency budget.
