Post-Training: A Practical Guide for AI Engineers and Developers

Capable by default. Reliable by design. If you're a practitioner who has watched a promising AI demo fail to survive contact with production, where prompting hits its ceiling, retrieval isn't enough, and the model still can't be trusted with your domain, post-training is what you've been missing. Post-Training is a practical guide to turning foundation models into production-ready systems — reshaping behavior, aligning to your values, and deploying with confidence. Each technique is taught concept-first, then implementation-through-code, so you understand not just what to run, but what you're actually changing inside the model. You'll leave with the skills to: Fine-tune models on curated datasets using supervised fine-tuning, LoRA, and QLoRA without destroying the base model's general capabilities Apply reinforcement learning from human feedback and modern preference optimization methods, including GRPO, ORPO, and beyond, to shape model behavior Evaluate models rigorously: design benchmarks, detect regression, and measure quality claims that survive scrutiny Adapt models to specialized domains, from clinical language to legal text, turning general capability into a defensible competitive advantage Train agentic models that take sequences of actions reliably, not just models that talk about taking actions Quantize and compress fine-tuned models for deployment without sacrificing the gains you trained for Post-training is where models stop being impressive and start being useful. This book teaches you to do it right.