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

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 verifiable rewards (RLVR) and modern preference optimisation methods including DPO, ORPO, and beyond, to shape model behaviour

Evaluate models rigorously: design benchmarks, detect regression, and measure quality claims that survive scrutiny

Adapt models to specialised 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

Quantise and compress fine-tuned models for deployment without sacrificing the gains you trained for

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, clearly documenting trade-offs and benefits.

From principles to practice.

Combines technical depth with strategic context. Includes companion Jupyter notebooks covering practical implementation. Shows how to embed proprietary knowledge, organisational values and domain expertise into foundation models.

Part I: The Foundation

• –Chapter 1: Post-Training Essentials: What It Is and Why It Matters
• –Chapter 2: Prerequisites for Success: Before You Fine-Tune

Part II: The Tools

• –Chapter 3: Supervised Fine-Tuning: The Foundation Technique
• –Chapter 4: Reinforcement Learning: Better Each Time
• –Chapter 5: Preference Optimization: Modern Alternatives to PPO
• –Chapter 6: Evaluation Strategies: Measuring Model Quality

Part III: The Craft

• –Chapter 7: Efficiency Techniques: Quantization and Compression
• –Chapter 8: Domain Adaptation: Make It Yours
• –Chapter 9: Agentic Models: Deeds, Not Words
• –Chapter 10: Reasoning Capabilities: Training for Complex Thought

Part IV: The Frontier

• –Chapter 11: Synthetic Training: Self-Play and Generated Data
• –Chapter 12: Multimodal Systems: Post-Training Beyond Text
• –Chapter 13: Future Directions: What Comes Next