Large Language Models: The Hard Parts by Thársis T.P. Souza (.ePUB)+
File Size: 31.0 MB
Large Language Models: The Hard Parts: Open Source AI Solutions for Common Pitfalls (Final Release) by Thársis T.P. Souza, Jonathan K. Regenstein, Jr
Requirements: .ePUB, .PDF reader, 31.0 MB | True PDF, True EPUB
Overview: Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. Large Language Models: The Hard Parts offers a clear, practical examination of the limitations developers and AI engineers face when building LLM-based applications. With a focus on implementation pitfalls (not just capabilities), this book provides actionable strategies supported by reproducible Python code and open source tools. Readers will learn how to navigate key obstacles in application evaluation, input management, testing, and safety. Designed for builders and technical product leads, this guide emphasizes practical solutions to real-world problems and promotes a grounded understanding of LLM constraints and trade-offs. In recent years, LLMs have emerged as a transformative force in technology. From ChatGPT and Gemini to Claude and Mistral, these systems have captured the public’s imagination and sparked a gold rush of AI-powered applications. However, beneath this technological revolution lies a complex landscape of challenges that developers, data scientists, and technical leaders must navigate. We wrote this book because we’re optimistic about the power and possibilities of LLMs but realistic about how hard it is to deploy them successfully, widely, and reliably. This book focuses on bringing awareness to key LLM challenges and harnessing open source solutions to overcome them. It offers a critical perspective on implementation, backed by practical and reproducible Python examples.
Genre: Non-Fiction > Tech & Devices
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