Before building applications that interact with Large Language Models, establishing a consistent and manageable development environment is a necessary first step. LLM projects often rely on specific versions of libraries and require careful handling of sensitive information like API keys. Getting the setup right from the start prevents issues later in the development cycle.

This chapter focuses on configuring your Python environment specifically for LLM tasks. We will cover:

• Selecting appropriate Python versions.
• Using virtual environments such as venv or conda to isolate project dependencies.
• Managing required packages effectively with pip and requirements.txt files.
• Installing libraries commonly used in LLM workflows.
• Implementing secure methods for storing and accessing API keys.

By the end of this chapter, you will have a practical understanding of how to set up and maintain a clean, reproducible development environment tailored for building LLM applications in Python.