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@sergiovaca95: #herido #miriamhernandez #luiismiguel #❤️ #cover

sergiovaca95
sergiovaca95
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Friday 14 July 2023 15:38:17 GMT
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alecitaaleli
ojitos bonitos :
q hermosa canción
2023-07-14 16:10:56
0
juliojosegonzalez33
Julio José González :
😍
2025-09-21 00:18:47
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vctor.u
Víctor u :
🥰
2025-10-17 00:31:46
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🌳 The LLM Cost Tree: Optimize Outcomes, Not Tokens Most teams try to reduce AI costs by negotiating cheaper tokens. That helps—but it rarely fixes the real problem. Your actual cost is closer to: Cost per success = tokens × model price × retries × tool loops A “cheap” model becomes expensive when it needs three retries. A smaller prompt becomes irrelevant if an agent loops 20 times. A powerful model is wasteful when the task only needs classification or extraction. The smarter approach is to optimize the entire execution path. ⚙️ 🧠 Spend less per call Use smaller models for predictable tasks, route by complexity, and escalate only when confidence is low. 📚 Send fewer tokens Trim irrelevant history, summarize long conversations, and retrieve only the evidence required for the current task. ✍️ Generate less Set output ceilings, request structured responses, and use deterministic tools when reasoning adds no value. ⚡ Avoid repeated work Cache exact responses, reusable prompt prefixes, and semantically equivalent requests. 🛡️ Control execution Batch asynchronous workloads, cap agent turns and tool calls, enforce timeouts, and track the cost of successful outcomes. The important engineering principle: The cheapest token does not guarantee the cheapest completed task. Measure what actually reaches production: ✅ Task success rate ✅ End-to-end latency ✅ Tokens consumed ✅ Tool calls and retries ✅ Human-review time ✅ Cost per successful outcome Because production AI optimization isn’t about making every request cheap. It’s about spending intelligence only where intelligence creates value. 🌱 Save this tree for your next AI architecture or cost-review meeting. 📌 #HackProduct #AIEngineering #LLM #GenerativeAI #AgenticAI
🌳 The LLM Cost Tree: Optimize Outcomes, Not Tokens Most teams try to reduce AI costs by negotiating cheaper tokens. That helps—but it rarely fixes the real problem. Your actual cost is closer to: Cost per success = tokens × model price × retries × tool loops A “cheap” model becomes expensive when it needs three retries. A smaller prompt becomes irrelevant if an agent loops 20 times. A powerful model is wasteful when the task only needs classification or extraction. The smarter approach is to optimize the entire execution path. ⚙️ 🧠 Spend less per call Use smaller models for predictable tasks, route by complexity, and escalate only when confidence is low. 📚 Send fewer tokens Trim irrelevant history, summarize long conversations, and retrieve only the evidence required for the current task. ✍️ Generate less Set output ceilings, request structured responses, and use deterministic tools when reasoning adds no value. ⚡ Avoid repeated work Cache exact responses, reusable prompt prefixes, and semantically equivalent requests. 🛡️ Control execution Batch asynchronous workloads, cap agent turns and tool calls, enforce timeouts, and track the cost of successful outcomes. The important engineering principle: The cheapest token does not guarantee the cheapest completed task. Measure what actually reaches production: ✅ Task success rate ✅ End-to-end latency ✅ Tokens consumed ✅ Tool calls and retries ✅ Human-review time ✅ Cost per successful outcome Because production AI optimization isn’t about making every request cheap. It’s about spending intelligence only where intelligence creates value. 🌱 Save this tree for your next AI architecture or cost-review meeting. 📌 #HackProduct #AIEngineering #LLM #GenerativeAI #AgenticAI

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