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Building LLM Powered  Applications
Building LLM Powered  Applications

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

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Building LLM Powered Applications

Base models versus customized models

The nice thing about Large Language Models is that they have been trained and ready to use. As we saw in the previous section, training an LLM requires great investment in hardware (GPUs or TPUs) and it might last for months, hardly feasible from individuals. Luckily, pre-trained LLM are generalized enough to be applicable at various tasks, so they can be consumed as they are directly via their REST API (we will dive deeper into model consumption in next chapters). Nevertheless, there might be scenarios where a general-purpose LLM is not enough, since it lacks domain-specific knowledge or doesn’t conform to a particular style and taxonomy of communication. If this is the case, you might want to customize your model.

How to customize your model

There are three main ways to customize your model:

  • Extending non-parametric knowledge. This allows the model to access external sources of information to integrate its parametric knowledge while responding...

Summary

In this chapter, we explored the field of Large Language Models, with a technical deep dive into their architecture, functioning and training process. We saw the most prominent architectures such as the transformer-based frameworks, how the training process works and different ways to customize your own LLM.We now have the foundation to understand what LLMs are. In next chapter, we will see to use them and, more specifically, how to build intelligent applications with them.

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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 22, 2024
Length: 342 pages
Edition : 1st
Language : English
ISBN-13 : 9781835462638
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Product Details

Publication date : May 22, 2024
Length: 342 pages
Edition : 1st
Language : English
ISBN-13 : 9781835462638
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Languages :

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Table of Contents

15 Chapters
Introduction to Large Language Models Chevron down icon Chevron up icon
LLMs for AI-Powered Applications Chevron down icon Chevron up icon
Choosing an LLM for Your Application Chevron down icon Chevron up icon
Prompt Engineering Chevron down icon Chevron up icon
Embedding LLMs within Your Applications Chevron down icon Chevron up icon
Building Conversational Applications Chevron down icon Chevron up icon
Search and Recommendation Engines with LLMs Chevron down icon Chevron up icon
Using LLMs with Structured Data Chevron down icon Chevron up icon
Working with Code Chevron down icon Chevron up icon
Building Multimodal Applications with LLMs Chevron down icon Chevron up icon
Fine-Tuning Large Language Models Chevron down icon Chevron up icon
Responsible AI Chevron down icon Chevron up icon
Emerging Trends and Innovations Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(22 Ratings)
5 star 54.5%
4 star 31.8%
3 star 0%
2 star 9.1%
1 star 4.5%
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Javier Dec 08, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great content!!
Subscriber review Packt
Dag Aug 24, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent introduction to engineering LLM-based applications. Bummer that the code examples won't work with current langchain versions. Then again, fiddeling around with that framework is just another learning opportunity.
Subscriber review Packt
Javier Soques Jul 04, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good learning resource for understanding LLMs
Feefo Verified review Feefo
dr t Jun 01, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As an advocate of LLMs, this looked like a book that I should read.The book is well-written, well-structured, and easy to read. In 13 chapters, it takes readers through fundamental concepts (e.g. transformers, backprop, embeddings), to practical applications (e.g. building a chatbot, search engines) and considerations (e.g. choosing an LLM, prompt engineering), ethical and responsible considerations, and also looks at the latest advancements in the field of gen AI.This reader found the chapter on Search and Recommendation Engines with LLMs most compelling. The chapter explores how large language models can improve recommendation systems through the use of embeddings and generative models, the reader also learns how to build their own recommendation system application, utilising state-of-the-art LLMs with LangChain as the framework. Fascinating stuff!In summary, if you work with LLMs this is a useful book that contains lot of useful knowledge and practical examples. Recommended.
Amazon Verified review Amazon
Saksham May 25, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
One of the standout features of this book is its emphasis on real-world applications. The author shares numerous case studies and practical examples that highlight how LLMs can be used to create intelligent applications across various domains, from customer service chatbots to advanced content generation tools. These examples are not just technically informative but also thoughtfully chosen to demonstrate the potential for LLMs to enhance human productivity and creativity.
Amazon Verified review Amazon
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