Overview

RLHF in Practice is the practical, no-nonsense guide that ML engineers and technical teams have been waiting for. This book takes you step-by-step through the real-world process of aligning and post-training large language models using human feedback. Instead of abstract theory, you'll get clear explanations, honest trade-offs, and actionable strategies you can apply immediately. You'll learn: Why SFT is the foundation of every successful alignment pipeline - and how to do it right How to collect high-quality human preference data that actually improves your model When to use Direct Preference Optimization (DPO) versus full PPO - and why most teams now prefer the simpler path How to build iterative, multi-stage pipelines that deliver reliable results Common failure modes (reward hacking, alignment tax, over-refusal) and exactly how to debug them Practical evaluation techniques that go beyond misleading benchmarks Scaling realities: data, compute, and infrastructure challenges at real production scale Ethical considerations, bias, and pluralistic alignment Perfect for engineers who want to move beyond tutorials and build production-grade aligned LLMs without wasting time on hype or overly complex approaches. Whether you're fine-tuning open models like Llama or Mistral derivatives, building internal tools, or preparing for large-scale deployment, this book gives you the practical knowledge and decision frameworks you need to succeed.

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