Equation 36 · Part 10 · Naive, Graph, and Agentic: A Systems Comparison of RAG Architectures

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L_{\text{total}} = L_{\text{enc}} + \sum_{i=1}^{k} \left( L_{\text{retrieve}}^{(i)} + L_{\text{LLM}}^{(i)} \right).

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What this part means

A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.

Its job in the formula

A raised mark can be a power or an index. Its position and the surrounding notation determine which.

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The passage around this formula

A single latency model makes the difference between the first three rows and the last two concrete. Writing $L_{\text{enc}}$ for query encoding, and $L_{\text{retrieve}}^{(i)}$ and $L_{\text{LLM}}^{(i)}$ for the retrieval and generation cost of round i , $L_{\text{total}} = L_{\text{enc}} + \sum_{i=1}^{k} \left( L_{\text{retrieve}}^{(i)} + L_{\text{LLM}}^{(i)} \right)$ . For naive and hybrid RAG, k = 1 by construction — the sum has exactly one term, and total latency is boundable in advance for any query. For iterative RAG, k is a small integer set by a heuristic or a step cap chosen by the system builder, so the worst case is known even though the typical case varies with question difficulty. For agentic RAG, k is a random variable generated by the policy itself at run time, and its…

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