What does this equation mean?

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 distribution is exactly what the operator does not control without adding an external cap. The same formula describes all five families; what changes is only where k comes from, and that difference is the whole of the engineering distinction between “a pipeline with a loop in it” and “a policy that decides how much to loop.”