Building LLM Powered Applications: Create intelligent apps and agents with large language models. Valentina Alto

Building LLM Powered Applications: Create intelligent apps and agents with large language models. Valentina Alto


  • Артикул: LB-0022556
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Key benefits

  • Embed LLMs into real-world applications
  • Use LangChain to orchestrate LLMs and their components within applications
  • Grasp basic and advanced techniques of prompt engineering

Description

Building LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.

Who is this book for?

Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.

What you will learn

  • Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings
  • Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM
  • Use AI orchestrators like LangChain, with Streamlit for the frontend
  • Get familiar with LLM components such as memory, prompts, and tools
  • Learn how to use non-parametric knowledge and vector databases
  • Understand the implications of LFMs for AI research and industry applications
  • Customize your LLMs with fine tuning
  • Learn about the ethical implications of LLM-powered applications
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Інформація про книгу
Обкладинка М'яка
Видавництво 1st Edition
Рік видання 2024
ISBN 9781835462638
Кількість сторінок 342
Мова видання Англійська

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