Overview

Stop treating LLMs like temperamental pets and start treating them like production services. I'm Adrian Vossner - engineer, architect, and occasional chaos-tamer - and I wrote the book I needed when my first ""proof-of-concept"" tried to schedule a meeting with the CEO (without approval). This is a hands-on, Python-first playbook for teams that must move fast and stay sane. No deep theory detours - just repeatable patterns, copy-paste artifacts, and realistic tradeoffs you can apply tomorrow. What you'll build and ship inside this book: - A repeatable enterprise blueprint (architecture diagrams, request flows, rollout checklists) - Build-along Python code: FastAPI services, ingestion workers, RAG with citations, tool layers, and a prompt registry - Practical fine-tuning recipes (PEFT / LoRA / QLoRA) and packaging for production - Ops + reliability playbook: tracing, replay debugging, cost dashboards, load tests, fallbacks, and circuit breakers - Security & compliance patterns: PII redaction, prompt-injection defenses, audit trails, tenant isolation - A capstone project you can clone: move from pilot to multi-team production with operational handoffs Who this book is for Backend engineers, ML engineers, SREs, and engineering leaders building enterprise generative AI applications with Python - especially if you care about cost, security, and getting from demo to dependable quickly. A quick promise: follow the capstone and templates and you'll have a secure RAG pilot and production-ready API scaffolding you can demo to stakeholders in days, not months. Ready to stop babysitting models and start shipping systems? Open the blueprint, roll up your sleeves, and let's build something your compliance team will actually approve. - Adrian Vossner

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