Large Language Models The Hard Parts Open Source AI Solutions for Common Pitfalls (True PDF,) | 16.6 MB
Title: Large Language Models: The Hard Parts
Author: Thrsis T. P. Souza;Jonathan K. Regenstein Jr.;
Category: Nonfiction, Computers, Advanced Computing, Natural Language Processing, Programming, General Computing
Language: English | 341 Pages | ISBN: 9798341622494
Description:
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.
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.
- Design testing and evaluation strategies for nondeterministic systems
- Manage context, RAG, and long-context retrieval
- Address output inconsistency and structural unreliability
- Implement safety and content moderation frameworks
- Explore alignment challenges and mitigation techniques
- Leverage open source models locally
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