Taming LLMs is a comprehensive guide that addresses critical challenges associated with Large Language Models (LLMs) in open-source software development. This project emphasizes understanding the limitations and implementation pitfalls that engineers and technical leaders encounter when creating LLM-powered applications.

Overview

The discourse surrounding LLMs often highlights their capabilities but frequently overlooks essential challenges. "Taming LLMs" aims to bridge this gap by offering practical insights into real-world issues faced during LLM implementation. Through illustrative Python examples and reliable open-source tools, this project presents an introductory yet thorough exploration of these challenges.

By focusing on tangible problems accompanied by reproducible code examples, the guide equips users with the necessary knowledge to develop products that leverage LLMs effectively while avoiding their inherent drawbacks.

Key Chapters

The guide is structured into several chapters, each addressing specific topics related to LLMs:

Chapter	PDF	Podcast	Website	Notebook	Status
Preface			View	N/A	Ready for Review
About the Book			View	N/A	Ready for Review
Chapter 1: The Evals Gap	Download	Listen	View	Notebook	Ready for Review
Chapter 2: Structured Output	Download	Listen	View	Notebook	Ready for Review
Chapter 3: Managing Input Data			View	Notebook
Chapter 4: Safety			View	Notebook
Chapter 5: Preference-Based Alignment			View	Notebook
Chapter 6: Local LLMs in Practice			View	Notebook
Chapter 7: The Falling Cost Paradox					Work in Progress
Chapter 8: Frontiers
Appendix A: Tools and Resources

This resource is particularly valuable for those looking to deepen their understanding of LLMs and how to effectively implement them in real-world projects.