Taming LLMs Cover

Taming LLMs

A Practical Guide to LLM Pitfalls with Open Source Software

Abstract: The current discourse around Large Language Models (LLMs) tends to focus heavily on their capabilities while glossing over fundamental challenges. Conversely, this book takes a critical look at the key limitations and implementation pitfalls that engineers and technical leaders encounter when building LLM-powered applications. Through practical Python examples and proven open source solutions, it provides an introductory yet comprehensive guide for navigating these challenges. The focus is on concrete problems with reproducible code examples and battle-tested open source tools. By understanding these pitfalls upfront, readers will be better equipped to build products that harness the power of LLMs while sidestepping their inherent limitations.


(*) The pdf version is preferred as it contains corrections and side notes.

Chapter

PDF

Podcast

Website

Notebook

Status

Preface

pdf

html

N/A

Ready for Review

About the Book

pdf

html

N/A

Ready for Review

Chapter 1: The Evals Gap

pdf

podcast

html

ipynb

Ready for Review

Chapter 2: Structured Output

pdf

podcast

html

ipynb

Ready for Review

Chapter 3: Managing Input Data

pdf

html

ipynb

Ready for Review

Chapter 4: Safety

pdf

html

ipynb

Ready for Review

Chapter 5: Preference-Based Alignment

html

ipynb

Chapter 6: Local LLMs in Practice

html

ipynb

Chapter 7: The Falling Cost Paradox

WIP

Chapter 8: Frontiers

Appendix A: Tools and Resources

CC BY-NC-SA 4.0

@misc{tharsistpsouza2024tamingllms,
  author = {Tharsis T. P. Souza},
  title = {Taming LLMs: A Practical Guide to LLM Pitfalls with Open Source Software},
  year = {2024},
  journal = {GitHub repository},
  url = {https://github.com/souzatharsis/tamingLLMs)
}