Large Language Models: The Hard Parts

Open Source AI Solutions for Common Pitfalls

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Beschreibung

<p>Large language models (LLMs) have transformed natural language processing, but deploying them in applications introduces numerous technical challenges. <em>Large Language Models: The Hard Parts</em> 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.</p> <p>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.</p> <ul> <li>Design testing and evaluation strategies for nondeterministic systems</li> <li>Manage context, RAG, &amp; long-context retrieval</li> <li>Address output inconsistency and structural unreliability</li> <li>Implement safety and content moderation frameworks</li> <li>Explore alignment challenges and mitigation techniques</li> <li>Leverage open source models locally</li></ul>

Artikeldetails

EAN

9798341622524

Sprache

Englisch

Einband / Art

Taschenbuch

Maße

231 x 175 x 20 mm

Erscheinungsjahr

2026

Verlag / Hersteller

O'Reilly Media