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Reranking and Retrieval Budgets

A reranker spends extra computation on retrieved candidates. Count query-document pairs, protect permission boundaries and measure whether better evidence justifies its added queueing and latency.

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RELATED CONCEPTS

Embedding Services and Vector Retrieval Evaluation Metrics and Data Splits Capacity and Backpressure Latency Metrics: TTFT, TPOT and Goodput Model Metadata and Artifact Storage

LESSONS THAT TEACH THIS

From Model to ServiceBuild retrieval as a budgeted pipeline12 min→

PRACTICE THIS IN REAL QUESTIONS

AI Infrastructure System DesignDesign a distributed search system with an LLM answer layer at 10,000 queries per second. Size both tiers and name the SLOs.→Open-Weights Models & Serving EnginesWould you split prefill and decode onto separate pools for a 753B model? Work the decision.→Open-Weights Models & Serving EnginesSet max-model-len and max-num-seqs for a chat product from first principles.→Open-Weights Models & Serving EnginesDesign a deployment that serves a trillion-parameter model at a million tokens of context with usable latency.→Open-Weights Models & Serving EnginesThree open-weights models could serve your product. How do you choose?→AI Infrastructure System DesignDesign a multi-region inference deployment: capacity per region, routing, failover, and getting the weights everywhere.→