Mastering LLM Fine-Tuning

Naim Rihan

ISBN 13: 9798258483485

Editore: Independently Published, 2026

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Dati bibliografici

• Titolo: Mastering LLM Fine-Tuning
• Editore: Independently Published
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798258483485
• Rilegatura: PAP
• Condizione: New
• Peso: 177 grammi

Descrizione articolo

Riassunto

Most people use AI. Very few know how to control it.

If you’ve ever felt that large language models are powerful—but unpredictable, inconsistent, or not tailored to your needs—this book is your turning point.

Mastering LLM Fine-Tuning is a practical, professional guide to transforming general-purpose AI models into specialized, high-performance systems that deliver real value in production.

Pre-trained models are impressive—but they are not enough.

In real-world applications, you need systems that are:

• consistent and reliable

• domain-aware and precise

• aligned with user intent

• optimized for real tasks—not just demos

This is exactly what fine-tuning enables.

What You Will Learn

This book goes beyond theory and focuses on real-world execution. You will learn how to:

• Understand how large language models actually work—and where they fail

• Build a complete fine-tuning pipeline from data to deployment

• Apply Supervised Fine-Tuning (SFT) for structured and predictable outputs

• Use RLHF to align models with human preferences and expectations

• Leverage LoRA and QLoRA to fine-tune efficiently—even with limited resources

• Design high-quality datasets that significantly improve model performance

• Evaluate models correctly and avoid misleading metrics

• Deploy, monitor, and scale systems using modern MLOps practices

• Combine fine-tuning with RAG, prompting, and AI agents for advanced systems

From Concepts to Real Systems

This book is designed to bridge the gap between understanding and building.

You won’t just learn techniques—you will learn how to:

• build domain-specific AI assistants

• fine-tune models for technical and coding tasks

• optimize performance under real-world constraints

• design systems that evolve and improve over time

Who This Book Is For

This book is ideal for:

• AI engineers and developers

• machine learning practitioners

• students transitioning from theory to real-world AI

• anyone serious about building production-ready AI systems

Why This Book Matters

In today’s AI landscape, the advantage no longer comes from simply using models.

It comes from knowing how to adapt, control, and optimize them.

Fine-tuning is one of the most valuable and in-demand skills in modern AI engineering—and this book gives you a clear, structured path to mastering it.

You can continue using AI tools as they are…
or you can learn how to shape them into systems that truly work.

This book shows you how.