This repository provides a practical guide to the challenges and pitfalls encountered when building applications with Large Language Models (LLMs). Aimed at engineers and technical leaders, it offers solutions using open-source software and Python examples to navigate common issues, enabling the development of more robust LLM-powered products.

How It Works

The guide addresses LLM limitations through a series of chapters, each focusing on a specific pitfall. It provides practical Python code examples and highlights battle-tested open-source tools to demonstrate concrete solutions. The approach emphasizes reproducible code and a critical examination of LLM capabilities versus implementation challenges.

Quick Start & Requirements

• Install/Run: No direct installation command is provided as this is a guide/book. Access to the content is via HTML links for chapters and .ipynb files for notebooks.
• Prerequisites: Python is used for code examples. Specific dependencies will vary per chapter's notebook.
• Resources: Requires a standard development environment capable of running Python notebooks.

Highlighted Details

• Covers critical LLM challenges including the "Evals Gap," structured output, input data management, safety, preference-based alignment, and local LLM deployment.
• Provides practical Python code examples and links to interactive Jupyter notebooks for hands-on learning.
• Discusses the "Falling Cost Paradox" and future "Frontiers" in LLM development.
• Includes an appendix of tools and resources for further exploration.

Maintenance & Community

The project is maintained by souzatharsis. Feedback and suggestions are encouraged via GitHub issues.

Licensing & Compatibility

The repository content is not explicitly licensed in the README. The use of open-source software within the examples implies adherence to those respective licenses. Compatibility for commercial use would depend on the licenses of the specific tools and libraries demonstrated.

Limitations & Caveats

This repository is a guide and not a runnable software project itself. The publication date is February 2, 2025, suggesting the content may be forward-looking or still in development. Specific LLM libraries and their versions used in the notebooks are not detailed, which could impact reproducibility if dependencies change.