Prompt Engineering for Production Systems. Dieser Artikel ist nicht verfügbar.Prompt Engineering for Production Systems

Shipping a working prompt takes an afternoon. Shipping it as a reliable, observable, audited production system takes a quarter — and most teams find that out the hard way.

You've felt this: a prompt that "worked yesterday" quietly starts producing worse answers today, and you find out from a customer support ticket, not from your own systems. The same input returns a different output, so you can't test it the way you test normal code. Nobody can say which prompt is actually running in production right now, or what changed since last week. Token costs climb faster than anyone budgeted for. A meaningful share of LLM calls fail outright, and a chain of agent calls multiplies that risk. And once user input starts flowing into your prompts, you've opened an attack surface most teams never see coming.

Prompt Engineering for Production Systems is the missing middle between abstract AI think-pieces and prompt cheat sheets. It treats prompts the way experienced engineers already treat code: as artifacts that must be versioned, tested, monitored, secured, and governed.

Across twelve chapters, you'll build the discipline that takes an LLM feature from "it worked in the demo" to something you can actually trust in production:

• Foundations — why prompts fail once they leave the prototype, and the production mindset that treats evaluation as a first-class engineering loop
• Engineering discipline — prompt architecture, getting prompts out of scattered string literals and into version control, building golden-dataset evaluation frameworks, and catching silent regressions in CI before they ship
• Reliability and scale — token economics and cost control, retries and circuit breakers for rate limits and failures, and the observability you need to catch drift before your customers do
• Advanced and organizational — debugging multi-step agent chains, defending against prompt injection, and the audit trails and governance regulated teams need

Every chapter follows the same rhythm: a real production failure, the underlying concept, a technical implementation with code you can adapt, a section written for the engineering manager or product lead who needs to understand what their team is wrestling with, and a reusable template or checklist. The appendices collect a full prompt pattern library, an evaluation checklist, and a production-readiness checklist you can apply directly to your own systems.

This book does not assume a research background. It assumes you can already get an LLM to do something useful, and that you're ready for the harder, less glamorous work of making it dependable — the work nobody warns you about when the demo goes well.

Who this is for: software engineers, ML practitioners, data scientists, and technical leads who have moved past the prototype and are responsible for an LLM system that has to actually work — reliably, measurably, and safely — in front of real users.