{ "item_title" : "Production LLM Architecture", "item_author" : [" Debabrata Acharjee "], "item_description" : "Your LLM prototype works. Production will cost you 10-50x more than it should.Most teams discover this after launch: the demo that cost cents per query now burns thousands per month, responses stall for 20 seconds before the first word appears, and failure modes surface that never existed in development.The problem is not the model. The problem is everything around the model: context that is too large, prompts reprocessed on every call, tasks routed to expensive models that cheaper ones handle just as well, agent loops with no bounds.Production LLM Architecture is the engineering layer above model selection. It defines six governing laws of production LLM systems (Prefill, Sequential Output, Prefix Stability, Information Density, Routing, Bounded State) and applies them across fourteen chapters spanning inference mechanics, context architecture, execution strategy, and deployment operations.What you will learn to do: Cut token usage 85-95% by stacking five compounding optimisation layers: data engineering, query-aware filtering, prompt caching, instruction minimalism, and tiered model routingReduce time-to-first-token from 8 seconds to under 1 second by engineering what enters the context windowDiagnose and fix the 24 documented production failure modes, each with symptom, root cause, and fixBuild evaluation pipelines that catch regressions before users doDecide when self-hosting beats the API, with breakeven analysis and GPU capacity planningEvery technique is grounded in production results: what breaks at scale, not what looks good in benchmarks.This is not a prompt engineering book. It is not a model survey. It is a systems engineering text for the layer where LLM applications succeed or fail: inference cost, latency ceilings, context management, and operational reliability.Written for senior engineers, platform engineers, and technical leads accountable for the latency, cost, and reliability of LLM-powered systems. Assumes you already know what LLMs are. Shows you how to ship them.Includes ten appendices: provider differences, token accounting, production checklist, batch vs real-time decision framework, self-hosted vs API breakeven analysis, GPU capacity planning, and a complete production reference architecture.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/818/811/9798188112561_b.jpg", "price_data" : { "retail_price" : "49.99", "online_price" : "49.99", "our_price" : "49.99", "club_price" : "49.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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