{"product_id":"9781718505209","title":"Post-Training: A Practical Guide for AI Engineers and Developers","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eVon Csefalvay, Chris\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNo Starch Press\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e9\/1\/2026\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eCapable by default. Reliable by design.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIf 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.\u003cbr\u003e\u003cbr\u003e\u003ci\u003ePost-Training\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003eYou'll leave with the skills to:\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eFine-tune models on curated datasets using supervised fine-tuning, LoRA, and QLoRA without destroying the base model's general capabilities\u003c\/li\u003e\n\u003cli\u003eApply reinforcement learning from human feedback and modern preference optimization methods, including GRPO, ORPO, and beyond, to shape model behavior\u003c\/li\u003e\n\u003cli\u003eEvaluate models rigorously: design benchmarks, detect regression, and measure quality claims that survive scrutiny\u003c\/li\u003e\n\u003cli\u003eAdapt models to specialized domains, from clinical language to legal text, turning general capability into a defensible competitive advantage\u003c\/li\u003e\n\u003cli\u003eTrain agentic models that take sequences of actions reliably, not just models that talk about taking actions\u003c\/li\u003e\n\u003cli\u003eQuantize and compress fine-tuned models for deployment without sacrificing the gains you trained for\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003ePost-training is where models stop being impressive and start being useful. This book teaches you to do it right.","brand":"No Starch Press","offers":[{"title":"Default Title","offer_id":48585115140351,"sku":"9781718505209","price":79.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/9781718505209_s600x595.jpg?v=1779819544","url":"https:\/\/massivebookshop.com\/de\/products\/9781718505209","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}