Designing Large Language Model Applications

Suhas Pai

Large language models (LLMs) have been proven 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.

The book offers practical advice on harnessing LLMs for use cases and dealing with commonly observed failure modes. The readers will 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.

The book is divided into 3 parts with a total of 13 chapters. The first part deals with understanding the ingredients of a language model. I strongly feel that even though you may never train a language model from scratch yourself, knowing what goes into making it is crucial. The second part discusses various ways to harness language models, be it by directly prompting the model, or by fine-tuning it in various ways. It also addresses limitations such as hallucinations and reasoning constraints, along with methods to mitigate these issues. Finally, the third part of the book deals with application paradigms like retrieval augmented generation (RAG) and agents, positioning LLMs within the broader context of an entire software system.