Large language model recipes : a hands-on guide to fine-tuning, optimization, deployment, and real-world applications /

The Large Language Model Recipes book is a comprehensive, practical guide designed to help developers, data scientists, and AI engineers navigate the rapidly evolving landscape of Large Language Models (LLMs). Moving beyond theory, this book provides a hands-on, recipe-based approach to mastering th...

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Bibliographic Details
Main Authors:	Bolla, Bharath Kumar (Author), Subbaiah, Kalpa (Author), Kaata, Sashi Kiran (Author)
Format:	eBook
Language:	English
Published:	Berkeley, CA : Apress, 2026.
Series:	Professional and Applied Computing Series.
Subjects:	Natural language processing (Computer science) Artificial intelligence. Traitement automatique des langues naturelles. Intelligence artificielle. artificial intelligence. Electronic books.

Table of Contents:

Part I: Setting Up Your AI Culinary Station
Chapter 1: An Introduction
Chapter 2: Environment Setup
Part II: Sourcing & Preparing Ingredients: Models & Data
Chapter 3: Open Source vs. Closed Source
Chapter 4: Data Handling & Tokenization
Part III: Mastering Core Techniques: Prompting & Fine-Tuning
Chapter 5: Prompt Engineering Mastery
Chapter 6: LLM Full Fine-Tuning
Chapter 7: Precision Seasoning: Instruction Fine-Tuning
Chapter 8: Parameter-Efficient Fine-Tuning (PEFT)
Chapter 9: Augmenting with Synthetic Data
Part IV: Optimization, Serving & Evaluation
Chapter 10: Making Models Leaner: Quantization Techniques
Chapter 11: LLM Deployment Strategies
Chapter 12: Evaluation Metrics & Benchmarks
Part V: Advanced Recipes & Future Flavors
Chapter 13: Retrieval-Augmented Generation (RAG)
Chapter 14: Exploring Multimodal Models
Chapter 15: Future Trends & Responsible AI
Appendix A: Glossary of LLM Terminology
Appendix B: Tooling Cheat Sheets (Hugging Face CLI & Libraries, PyTorch Essentials, LangChain Basics)
Appendix C: Curated List of Datasets, Model Hubs, and Further Reading.