Designing Large Language Model Applications: A Holistic Approach to LLMs

by Suhas Pai

Large language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note. But transitioning from demos and prototypes to full-fledged applications can be difficult. This book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language models.

Experienced ML researcher Suhas Pai offers practical advice on harnessing LLMs for your use cases and dealing with commonly observed failure modes. You’ll take a comprehensive deep dive into the ingredients that make up a language model, explore various techniques for customizing them such as fine-tuning, learn about application paradigms like RAG (retrieval-augmented generation) and agents, and more.



Understand how to prepare datasets for training and fine-tuningDevelop an intuition about the Transformer architecture and its variantsAdapt pretrained language models to your own domain and use casesLearn effective techniques for fine-tuning, domain adaptation, and inference optimizationInterface language models with external tools and data and integrate them into an existing software ecosystem

Genres: Artificial Intelligence, Technology

364 pages, Paperback

Published April 15, 2025

Reviews

[Khan · Rating 4 out of 5]

3.9

[Josua Naiborhu · Rating 4 out of 5]

This book is good for getting into understanding how llm-based application constructed from the dataset construction along the way to deployment process. However, the written resources do not provide clear instruction with the github repo on the book in a structural way. It is better off constructing each chapter alongside the folder in github repo for making it much more structural content.