Building with LLMs: A Practical Engineer's Guide to Shipping Reliable, Scalable, and Maintainable AI Applications

You've built a prototype that works beautifully in a notebook. But when three users hit it simultaneously, it falls apart. The cost of API calls is eating your margins. The model fabricates answers that look convincing enough to ship. These are not bugs; they are fundamental properties of working with Large Language Models, and they require a new engineering discipline to address.

This book is written for the engineer who needs to ship something real. Not a demo, not a proof of concept, but a production system that users rely on. It provides a practical framework for making decisions about when and how to use LLMs, covering the entire lifecycle from model selection and prompt design to evaluation, monitoring, and scaling. This is not a catalog of tools; it is a set of principles and patterns that survive the rapid changes in the AI landscape.

Inside, you'll discover:

• The mindset shift required to manage nondeterministic systems

• How to choose the right model stack for cost and performance

• Prompt engineering strategies that work at production scale

• Building a RAG pipeline that actually retrieves relevant context

• Evaluation-driven development for continuous improvement

• Optimizing cost, latency, and caching strategies

• Implementing security, safety, and guardrails

The tools will keep changing, but the problems of reliability, cost, and safety will remain. This book gives you the foundational knowledge to build AI applications that don't just work in a demo, but thrive in the real world.