Thársis T. P. Souza, Jonathan K. Regenstein Jr. | Large Language Models: The Hard Parts. Open Source AI Solutions for Common Pitfalls (2026) [PDF, EPUB]
Автор: Thársis T. P. Souza, Jonathan K. Regenstein Jr.
Издательство: O'Reilly Media
ISBN: 979-8341622524
Жанр: Natural Language Processing, Software Development, Computer Science
Язык: Английский
Формат: PDF, EPUB
Качество: Изначально электронное (ebook)
Иллюстрации: Цветные и черно-белые
Описание: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, & 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
Foreword xiii
Preface xv
1. First Principles: What to Consider Before We Start Building with LLMs 1
2. The Evals Gap 17
3. Evaluation Tools for LLM-Based Applications 53
4. From Data to Context 83
5. Structured Data Output 133
6. LLM Safety Considerations 161
7. Evaluating LLMs for Safety 179
8. LLM Alignment: A Case Study 227
9. Epilogue: LLMBAs in the Era of Falling Costs 279
Appendix. Tools for Local LLM Deployment 283
Index 303
Скриншоты:
Время раздачи: с 10 до 20 (минимум до появления первых 3-5 скачавших) 
