, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications

Key Features

Embed LLMs into real-world applicationsUse LangChain to orchestrate LLMs and their components within applicationsGrasp 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 readers 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

Core components of LLMs' architecture, including encoder-decoders blocks, embedding and so onGet well-versed with unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLMUse AI orchestrators like LangChain, and Streamlit as frontendGet familiar with LLMs components such as memory, prompts and toolsLearn non-parametric knowledge, embeddings and vector databasesUnderstand the implications of LFMs for AI research, and industry applicationsCustomize your LLMs with fine tuningLearn 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.