Prompt Engineering for Llms

Auteur

John Berryman is the founder and principal consultant of Arcturus Labs, where he specializes in LLM application development. His expertise helps businesses harness the power of advanced AI technologies. As an early engineer on GitHub Copilot, John contributed to the development of its completions and chat functionalities, working at the forefront of AI-assisted coding tools.

Before his work on Copilot, John built an impressive career as a search engineer. His diverse experience includes helping to develop next-generation search system for the US Patent Office, building search and recommendations for Eventbrite, and contributing to GitHub's code search infrastructure. John is also coauthor of Relevant Search (Manning), a book that distills his expertise in the field.

John's unique background, spanning both cutting-edge AI applications and foundational search technologies, positions him at the forefront of innovation in LLM applications and information retrieval.

Texte du rabat

Large language models (LLMs) are revolutionizing the world, promising to automate tasks and solve complex problems. A new generation of software applications are using these models as building blocks to unlock new potential in almost every domain, but reliably accessing these capabilities requires new skills. This book will teach you the art and science of prompt engineering-the key to unlocking the true potential of LLMs.

Industry experts John Berryman and Albert Ziegler share how to communicate effectively with AI, transforming your ideas into a language model-friendly format. By learning both the philosophical foundation and practical techniques, you'll be equipped with the knowledge and confidence to build the next generation of LLM-powered applications.

Understand LLM architecture and learn how to best interact with it Design a complete prompt-crafting strategy for an application Gather, triage, and present context elements to make an efficient prompt Master specific prompt-crafting techniques like few-shot learning, chain-of-thought prompting, and RAG