Building LLM Powered Applications

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

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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

After completing her bachelor's degree in finance, Valentina Alto pursued a master's degree in data science in 2021. She began her professional career at Microsoft as an Azure Solution Specialist, and since 2022, she has been primarily focused on working with Data & AI solutions in the Manufacturing and Pharmaceutical industries. Valentina collaborates closely with system integrators on customer projects, with a particular emphasis on deploying cloud architectures that incorporate modern data platforms, data mesh frameworks, and applications of Machine Learning and Artificial Intelligence. Alongside her academic journey, she has been actively writing technical articles on Statistics, Machine Learning, Deep Learning, and AI for various publications, driven by her passion for AI and Python programming.