# Amazon description — Mastering Large Language Models: From Theory to Production: Complete Guide for Developers, Data Scientists, and AI Engineers
# ASIN: B0GRGR4DHP
# Series: The 1'st AI Engineering Series

"Mastering Large Language Models: From Theory to Production" is the definitive guide for anyone who wants to deeply understand and implement large language models in production. Written by Picu Bianca-Maria, this book offers a "Senior Architect" perspective on the entire LLM lifecycle, from theoretical foundations to complex production implementations. The book covers all essential aspects: from Transformer architecture and pre-training, to fine-tuning, prompt engineering, RAG, optimization, deployment, scaling, and security. Each chapter is an original synthesis, based on a vast bibliography, but reformulated with unique analogies and critical comparisons.

What you will learn:
• Foundations: Deep understanding of Transformer architecture and language model evolution
• Training: Pre-training and fine-tuning, including PEFT techniques like LoRA and QLoRA
• Prompt Engineering: From fundamentals to advanced techniques (chain-of-thought, tree-of-thought)
• RAG: Implementing Retrieval-Augmented Generation systems
• Optimization: Quantization, pruning, distillation for efficiency
• Deployment: Deployment strategies, API design, testing
• Scaling: Distributed training and inference
• Monitoring: Observability, logging, alerting for LLM systems
• Cost Management: Cost optimization and resource management
• Security: Protection against attacks, prompt injection, best practices
• Evaluation: Metrics, benchmarks, A/B testing
• Case Studies: Real-world implementations and lessons learned
• Future: Emerging trends and new architectures

This book is the essential resource for developers, data scientists, and AI engineers who want to build robust, scalable, and efficient LLM systems.