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.
The Craft of 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. |
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