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 the entire LLMs lifecycle, from selecting the right open-source model to fine-tuning it on custom data and deploying it for production at scale.

Starting with the fundamentals of setting up a robust development environment, the book guides you through the critical decisions of model selection (Llama, Mistral, Falcon) and data preparation. It offers deep dives into advanced training techniques, including full fine-tuning, instruction tuning, and parameter-efficient methods like LoRA and QLoRA that make training accessible on consumer hardware.

The book doesn't stop at training. It tackles the crucial "last mile" of AI development: deployment and optimization. You will learn how to shrink models with quantization, serve them with high-throughput engines like vLLM and TGI, and evaluate their performance using industry-standard benchmarks. Finally, it explores cutting-edge frontiers, including Retrieval-Augmented Generation (RAG) for grounding models in real-time data, building multimodal vision-language applications, and designing autonomous AI agents.

Whether you are building a specialized chatbot, a code assistant, or a complex reasoning agent, this book provides the tested recipes and code you need to develop efficient, scalable, and robust AI solutions today.

What you will learn:

• Design production-ready LLM systems using the Feature/Training/Inference (FTI) framework
• Apply advanced fine-tuning methods, including LoRA and QLoRA, for efficient model adaptation
• Build and optimize RAG pipelines with effective retrieval strategies and vector databases
• Deploy optimized LLMs using quantization techniques and scalable inference frameworks
• Develop multimodal and agentic AI applications with vision-language models and autonomous agents

Who this book is for:

This book is ideal for software developers, machine learning engineers, data scientists, and technical researchers who want to move beyond using API endpoints and start

Bharath Kumar Bolla is an accomplished data scientist, researcher, and mentor with over 12 years of expertise in statistical analysis, machine learning, and natural language processing. Named among the "40 Under 40 Data Scientists" by Analytics India Magazine, he has successfully led teams in developing AI-driven solutions across the telecom, healthcare, marketing, and ed-tech sectors.

His work includes building advanced recommendation systems, optimizing pricing models that delivered multimillion-dollar ROIs, and pioneering sentiment analysis tools. With over twenty peer-reviewed publications in computer vision, forecasting, and NLP, Bharath is also a dedicated academic. He holds advanced degrees in data science, applied statistics, and life sciences from leading institutions. A passionate advocate for continuous learning, he frequently speaks at industry forums and serves as a technical reviewer for leading AI publications. His expertise in deep learning frameworks, statistical modelling, and scalable AI solutions positions him as a thought leader and innovator in the field.

Kalpa Subbaiah is a seasoned data scientist and AI expert with over 16 years of experience, including 8 years in data science and machine learning. Holding a Master’s in Machine Learning and AI from Liverpool John Moores University, she specializes in deploying end-to-end AI solutions on Azure Machine Learning, Databricks, and AWS. Her expertise spans computer vision, NLP, and deep learning frameworks like TensorFlow, PyTorch, and Keras. A certified Azure Data Scientist, AI Engineer, and AWS ML Specialist, Kalpa has designed scalable AI pipelines, MLOps solutions, and cutting-edge projects in sentiment analysis, object detection, and smart city solutions.

As a technical trainer and mentor, she delivers corporate and academic training worldwide, contributing through blogs, workshops, and community engagements. Currently, as Vice President and Lead Data Scientist at JPMorgan Chase & Co., she spearheads AI/ML initiatives, driving innovation and strategic AI advancements.