Large Language Models: The Hard Parts Open Source AI Solutions for Common Pitfalls - Thársis T. P. Souza Audiobook

Written by Thársis T. P. Souza
Read by Rich Miller
Format: M4B
Bitrate: 128 Kbps
Unabridged

Length: 8 hrs and 39 mins
Release date: 09-15-26

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.