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And how OKF fixes that. #
Most teams building AI-powered products in 2026 are running some version of RAG, short for Retrieval-Augmented Generation. The pattern is familiar: take a user question, search a vector database for related text chunks, stuff those chunks into a prompt, and let the model generate an answer.
The pattern works.
… Until it doesn’t.
I’ve watched teams burn weeks debugging answers that were correct on Tuesday and wrong on Thursday, with no change to the underlying documents. The problem was not the model. The problem was the retrieval layer. RAG treats your company’s knowledge like a pile of puzzle pieces thrown into a bucket. Every time you need an answer, you reach in, grab a handful, and hope you got the right ones.
That metaphor is not an exaggeration. It is the architecture.
Where RAG Breaks Down #
RAG has five failure modes that compound in production.
The first is Redundant Retrieval: the system fetches the same stable facts (your company’s pricing, your product’s architecture, your compliance requirements) on every single query, burning tokens and latency on information that has not changed in months.