Get hands-on with GPT 3.5, GPT 4, Lang - Chain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications - Key Features - Embed LLMs into real-world applications - Use Lang - Chain to orchestrate LLMs and their components within applications - Grasp basic and advanced techniques of prompt engineering - Book Description - Building LLM Apps 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. Moving ahead, with a focus on the Python-based, lightweight framework called Lang - Chain. 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 on - Get well-versed with unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLMUse AI orchestrators like Lang - Chain, and Streamlit as frontend - Get familiar with LLMs components such as memory, prompts and tools - Learn non-parametric knowledge, embeddings and vector databases - Understand the implications of LFMs for AI research, and industry applications - Customize your LLMs with fine tuning - Learn 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. But readers should have core ML/software engineering fundamentals to understand and apply the content.