From foundation model to production system: the complete guide to post-training.
Foundation models like GPT, Claude, and Llama have become commodities—available to every organisation through APIs or open weights. Yet most enterprises struggle to derive lasting competitive advantage from AI. The missing piece is post-training: the techniques that transform generic foundation models into specialised, aligned, production-ready systems tailored to specific business needs.
This book bridges the gap between academic research and practical implementation, covering supervised fine-tuning, RLHF and modern preference optimisation methods, evaluation discipline, efficiency techniques, domain adaptation, tool use and agency, and more. Spanning 13 chapters across 4 parts, it provides both the technical depth ML engineers need for implementation and the strategic context technical leaders require for investment decisions.
The Craft of Post-Training will be published by No Starch Press and available in digital early access in late Spring 2026, in print in early Summer 2026.

SFT is the ubiquitous starting point of most post-training. So let's get the most out of it.
RLHF, PPO and modern preference optimisation methods including DPO, KTO and GRPO(+).
Building robust benchmarks, model-based evaluation and avoiding common pitfalls in measuring quality.
LoRA, QLoRA, quantisation and knowledge distillation for deployment at scale.
Specialising models for industries and enabling interaction with external systems through function calling.
Synthetic data generation, RLAIF, vision-language alignment and emerging frontiers.
Explore all 13 chapters across 4 parts

The literature on post-training is focused either on small educational use cases that do not consider enterprise realities, or presuppose the workflow of foundation labs. There's nothing for the crucial middle: enterprise practitioners with real compute budgets who need to customise, align and deploy AI at scale. This book fills that gap.
The book treats post-training decisions as trade-offs rather than best practices, helping practitioners match techniques to constraints. It provides decision frameworks for the right techniques, clearly documenting trade-offs and benefits.
Combines technical depth with strategic context. Includes 12 companion Jupyter notebooks covering practical implementation. Shows how to embed proprietary knowledge, organisational values and domain expertise into foundation models.
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