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Building LLM Powered Applications Cover

Building LLM Powered Applications

Create intelligent apps and agents with large language models

Publisher:Packt Publishing Limited
By:   
Paid access
|Sep 2024
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Description

Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications

Key Features

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

Book 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.

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

Who this book is 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.

Table of Contents

  1. Introduction to Large Language Models
  2. LLMs for AI-Powered Applications
  3. Choosing an LLM for Your Application
  4. Prompt Engineering
  5. Embedding LLMs within Your Applications
  6. Building Conversational Applications
  7. Search and Recommendation Engines with LLMs
  8. Using LLMs with Structured Data
  9. Working with Code
  10. Building Multimodal Applications with LLMs
  11. Fine-Tuning Large Language Models
  12. Responsible AI
  13. Emerging Trends and Innovations
https://github.com/PacktPublishing/Building-LLM-Powered-Applications
PDF ISBN: 978-1-83546-263-8
Publisher: Packt Publishing Limited
Copyright owner: © 2024 Packt Publishing Limited
Publication date: 2024
Language: English
Pages: 342
Related subjects:
Computer sciences,
Computer sciences, other
BISAC subjects:
COM058000,
COM044000,
COM042000
Thema subjects:
UFD,
UYQN,
UYQL

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