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Building LLMs for production enhancing LLM abilities and reliability with prompting, fine-tuning, and RAG

With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LL...

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Bibliographic Details
Main Authors: Bouchard, Louis-François, Peters, Louie (Author)
Format: eBook
Language:English
Published: [Place of publication not identified] Towards AI 2024
Edition:First edition
Subjects:
Traitement Automatique Des Langues Naturelles
Artificial Intelligence / Aat
Artificial Intelligence / Http://id.loc.gov/authorities/subjects/sh85008180
Intelligence Artificielle
Natural Language Processing (computer Science) / Http://id.loc.gov/authorities/subjects/sh88002425
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
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520 |a With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future. The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python 

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