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Taming LLMs

Publication Date: February 2, 2025

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 product managers 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 - from handling unstructured output to managing context windows - 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.

Chapter	Website	Notebook	Status
Chapter 1: Introduction	html	N/A	Ready for Review
Chapter 2: Wrestling with Structured Output	html	ipynb	Ready for Review
Chapter 3: The Input Data Challenge
Chapter 4: Output Size Limitations	html	ipynb	Ready for Review
Chapter 5: The Evals Gap	html	ipynb	Ready for Review
Chapter 6: Safety Concerns	html		WIP
Chapter 7: Preference-based Alignment	html	ipynb	Ready for Review
Chapter 8: Breaking Free from Cloud Providers
Chapter 9: The Cost Factor
Chapter 10: Frontiers
Appendix A: Tools and Resources

Citation

@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)
}