Six governing laws. Fourteen chapters of production-tested techniques. Cut API costs from $11,000/month toward $400, best case near $160, and perceived latency from 17 seconds to under 2.
324 pages, 7×10 · Or try the calculator first.
Every chapter in the book references which laws it addresses. Every failure mode is tagged by the law it violates.
Input token count determines time-to-first-token.
Output tokens generate one at a time, and cost roughly 5x input.
Cache reuse requires exact token-for-token prefix match.
Remove redundancy from context, never structure.
Match model tier to task difficulty: excess capability is waste.
Context that grows without bounds hits latency and cost walls.
Every technique is grounded in production results: what actually breaks at scale, not what looks good in benchmarks.
Prefill and decode, token economics, and how the KV cache creates both the cost problem and its solution.
Data engineering, query-aware filtering, prompt caching, instruction minimalism, and tiered model routing.
Pipeline orchestration, tool use and agents, evaluation frameworks, retrieval architecture, and guardrails.
Graduated rollout, production monitoring, cost anomaly detection, and a catalog of 24 documented failure modes.
Two calculators built from the book's own formulas: no signup required.
See exactly where your API bill comes from: input, cached input, and output cost broken out per request, per day, and per month.
Find the request volume where GPU inference beats API calls, including the MLOps overhead most comparisons leave out.
"It's dense in the best way"
"Rather than being another rehash of prompt engineering tips, it's organised around six actual laws that describe how production LLM systems behave […] so by the end you understand why your system behaves the way it does under load, why latency shows up where it does, and why certain routing choices quietly blow your costs up. It's the kind of understanding people usually only get by learning it the hard way, in production, at 2 am.
"I'd point this squarely at senior engineers and tech leads who already have systems running in production and want to get serious about cost and performance instead of guessing at either. It's dense in the best way, so keep a notebook nearby as you read."
Fred Momeni · Senior Software Engineer, Canva · LinkedIn, August 2026
"A Must-Read for Engineers Building AI in Production"
"An excellent and highly practical guide to building production-ready LLM systems. The author has done a fantastic job of simplifying complex architectural concepts and presenting them in a clear, structured, and genuinely useful way. […]
"A very well-written and insightful handbook, and a great resource for anyone serious about building robust AI systems in production. Highly recommended!"
Debbie · ★★★★★ · Amazon review, August 2026
Fourteen chapters, ten reference appendices, and 24 documented failure modes: the engineering layer above model selection.