Designing Large Language Model Applications - by Suhas Pai (Paperback)

About the Book

"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"--

Book Synopsis

Transformer-based language models are powerful tools for solving a variety of language tasks and represent a phase shift in the field of natural language processing. But the transition from demos and prototypes to full-fledged applications has been slow. With this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models.

Experienced ML researcher Suhas Pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state-of-the-art models. You'll take a comprehensive deep dive into the Transformer architecture and its variants. And you'll get up-to-date with the taxonomy of language models, which can offer insight into which models are better at which tasks.

You'll learn:

• Clever ways to deal with failure modes of current state-of-the-art language models, and methods to exploit their strengths for building useful products
• How to develop an intuition about the Transformer architecture and the impact of each architectural decision
• Ways to adapt pretrained language models to your own domain and use cases
• How to select a language model for your domain and task from among the choices available, and how to deal with the build-versus-buy conundrum
• Effective fine-tuning and parameter efficient fine-tuning, and few-shot and zero-shot learning techniques
• How to interface language models with external tools and integrate them into an existing software ecosystem