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.

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 front end.
• 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. However, readers should have core ML/software engineering fundamentals to understand and apply the content.