An assistant built on a language model often works on the first try and then breaks in production: it answers with the wrong data, an untrusted attachment talks it into revealing its instructions, it burns through a token budget nobody planned for, or it runs an irreversible action that no one approved.
This book goes through the decisions every LLM-based system has to face before it can hold up in production, from handling tokens in the first API call to multi-user audit trails. It is split into four parts, Foundations, Control, Architecture and Production, and covers prompting as something you measure, reliable structured output, defences against untrusted input and prompt injection, human approval for risky actions, resilience and observability, RAG, agentic patterns, MCP, orchestration and model routing, testing and evals, cost control, security at scale, and ethical and legal concerns.
Every principle comes with real code written with Laravel AI, applied to a single running example: a personal finance assistant that reads bank statements, categorises expenses, proposes financial actions and asks for confirmation before carrying them out. Laravel AI is how the ideas are shown, not the subject of the book: the principles hold with a different framework or model provider too.