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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.

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(*) 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

@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)
}