Large Language Models: The Hard Parts
Open Source AI Solutions for Common Pitfalls
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
聴き放題対象外タイトルです。プレミアム会員登録で、非会員価格の30％OFFで予約注文できます。聴けるのは配信日からとなります。
Large Language Models: The Hard Parts
￥1,800で今すぐ予約注文する
-
ナレーター：
-
Rich Miller
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.
Listeners 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.
Discover how to 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; and leverage open source models locally.
©2026 Tharsis T. P. Souza and Jonathan K. Regenstein, Jr.