33How do you optimize a RAG or agent system for cost and latency in production?▼mediumCohereMicrosoftGlean2 replies◆ premiumRAG and agents get expensive and slow fast: retrieval plus reranking plus big-model calls, multiplied across agent steps. The signal is naming the dominant cost first, then the levers that actually move it. Here is the playbook.Open full answer →
09How do you optimize the cost of large-scale ML training and inference?▼mediumAmazonMicrosoftDatabricks2 repliesunlockedGPU spend is usually the biggest line item in an AI org, and this question checks whether you think in utilization, not just capacity. The signal is squeezing per-unit cost (utilization, right-sizing, spot, quantization) before scaling out.Open full answer →
12How do prompt caching and semantic caching cut LLM cost and latency, and what are the risks?▼mediumAnthropicOpenAIMicrosoft2 replies○ sign inCaching is one of the biggest LLM cost levers, but 'cache the response' is naive for a non-deterministic system. The signal is distinguishing prompt (prefix) caching from semantic caching and knowing when each is safe.Open full answer →
14What is LLM routing (model cascades / semantic routing), and how do you implement it?▼mediumMicrosoftDatabricksCohere1 replies○ sign inRouting each request to the right model is one of the biggest LLM cost/latency levers in production. The signal is matching query difficulty to model capability and knowing when the cascade pattern beats a classifier.Open full answer →
