The Craft of Post-Training.

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.

Supervised Fine-Tuning

SFT is the ubiquitous starting point of most post-training. So let's get the most out of it.

Reinforcement Learning from Human Feedback

RLHF, PPO and modern preference optimisation methods including DPO, KTO and GRPO(+).

Evaluation Discipline

Building robust benchmarks, model-based evaluation and avoiding common pitfalls in measuring quality.

Efficiency Techniques

LoRA, QLoRA, quantisation and knowledge distillation for deployment at scale.

Domain Adaptation & Tool Use

Specialising models for industries and enabling interaction with external systems through function calling.

Self-Play & Multimodal

Synthetic data generation, RLAIF, vision-language alignment and emerging frontiers.

Explore all 13 chapters across 4 parts

Hand-drawn illustration of a drill boring through stacked layers

Why this book?

Written With the Enterprise Practitioner In Mind.

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.

Trade-offs, Not Best Practices.

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.

From Principles to Practice.

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.

About the author

Chris von Csefalvay is a Principal at HCLTech's AI practice, where he specializes in research and clinical intelligence. He brings the dual perspectives of a post-training researcher and an enterprise AI practitioner to the task of advising the world's leading companies on AI strategy and implementation. His clients include four of the world’s five largest medical technology companies. He has advised leading enterprises including Apple, Netflix, Volkswagen, GE Healthcare, Sony Entertainment and Teradyne on their most demanding AI engagements.

In a career spanning nearly two decades, he has served in senior data science leadership roles across leading enterprises, published extensively on distributed computing for ML and designed language models for applications from pharmacovigilance to social dynamics. He is also the author of Computational Modeling of Infectious Disease, a leading monograph on computational epidemiology, the author of numerous peer reviewed papers and co-creator of one of the largest curated data sets on COVID-19.

He holds undergraduate and graduate degrees from the University of Oxford and Cardiff University, and is a Fellow of the Royal Society for Public Health and Senior Member of IEEE.

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