Designing Large Language Model Applications: A Holistic Approach - Softcover

Pai, Suhas

Softcover

ISBN 10: 1098150503 ISBN 13: 9781098150501

Publisher: O'Reilly Media, 2025

Synopsis

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-tuning
• Develop an intuition about the Transformer architecture and its variants
• Adapt pretrained language models to your own domain and use cases
• Learn effective techniques for fine-tuning, domain adaptation, and inference optimization
• Interface language models with external tools and data and integrate them into an existing software ecosystem

About this title

About the Author

Suhas Pai is an experienced machine learning researcher, having worked in the tech industry for over a decade. He is the co-founder, CTO, and ML Research Lead at Bedrock AI, a Y-Combinator backed NLP startup operating in the financial domain. At Bedrock AI, Suhas invented several novel NLP techniques and LM based architectures that fully power the core features of Bedrock AIas products. Suhas is also the co-chair of the Privacy Working Group at Big Science for the BLOOM language model project, which when released was the world's largest open-source multilingual language model.

Suhas is active in the ML community, being the Chair of the TMLS (Toronto Machine Learning Summit) ML conference in 2022, as well as the Chair of the TMLS NLP conference in 2021 and 2022. He is also the NLP lead at Aggregate Intellect, an ML community research organization, where he previously wrote a weekly AI newsletter summarizing and contextualizing the latest papers in ML (audience: 3k+) and hosted a weekly seminar to walk through recent NLP papers.

Edition details

• Publisher: O'Reilly Media
• Publication date: 2025
• Language: English
• ISBN 10: 1098150503
• ISBN 13: 9781098150501
• Binding: Paperback
• Number of pages: 364