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Abstract #
Context: Production RAG often fails when evidence, not models, breaks.Problem: Query-flow components remain under-instrumented and weakly coordinated.Approach: Decompose RAG into routing, embedding, retrieval, prompt construction, and validation.Results: Demo reached 0.91 macro-F1 and Recall@5=1.00, yet exposed top-rank mismatch.Conclusion: Treat RAG as an evidence supply chain, not a prompt trick.
Keyword: Retrieval-Augmented Generation; vector database; enterprise AI; AI search; Agentic RAG
The Query Is Not Just a Question: It Is the Control Signal of RAG #
A RAG system rarely fails because the language model “does not know.” More often, it fails because the right evidence never reaches the model, arrives in the wrong form, arrives too late, or is buried under irrelevant context. This is the uncomfortable truth behind many production RAG failures: the LLM is usually blamed for hallucination, but the real defect often lives upstream, in the query flow. The user asks one sentence; the system must transform that sentence into a retrieval operation, locate the most relevant knowledge fragments, assemble them into a constrained prompt, and force the model to answer within the boundaries of retrieved evidence. In other words, RAG is not simply “LLM plus vector database.” It is a…