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Fine-Tuning and Alignment Engineering: LoRA, DPO, RLHF, and Domain Adaptation for Production LLMs
Paperback
Series: Production AI Engineering
Publisher: Independently Published
Published: Jul 1 2026
Pages: 92
Weight: 0.30
Height: 0.19 Width: 6.00 Depth: 9.00
Language: English
In the rapidly evolving world of artificial intelligence, building generic models is no longer enough. Fine-Tuning and Alignment Engineering is your comprehensive, hands-on guide to taking pre-trained large language models and tailoring them to deliver enterprise-grade performance, safety, and domain-specific expertise.
Written specifically for machine learning engineers, AI researchers, and technical leaders, this practical handbook bypasses theoretical abstraction to deliver production-ready patterns, mathematical foundations, and real-world code structures for 2026 and beyond.
Inside this end-to-end engineering handbook, you will discover:- Parameter-Efficient Fine-Tuning (PEFT): Master the mathematics and implementation of LoRA, QLoRA, and weight updates on single A100 GPUs.
- Advanced Alignment Techniques: Dive deep into Direct Preference Optimization (DPO), Reinforcement Learning from Human Feedback (RLHF), PPO, and Group Relative Policy Optimization (GRPO).
- Domain Adaptation: Implement highly secure, compliant adaptations for complex legal, medical, and coding environments.
- Production Deployment: Architect low-latency serving pipelines using vLLM, TensorRT-LLM, speculative decoding, and multi-LoRA routing.
- MLOps & Governance: Build robust validation harnesses, evaluation pipelines, and continuous feedback loops.
Whether you are fine-tuning specialized medical assistants or scaling a code-generation platform, this guide provides the exact blueprints required to design, train, and deploy high-performing, aligned LLMs at scale. Transform your organization's AI strategy from standard API wrapper to proprietary powerhouse today!
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