The landscape of artificial intelligence has undergone a profound transformation in the past decade, one that has elevated large language models from experimental curiosities to indispensable components of modern technology. What began as modest neural networks capable of predicting the next word in a sentence has evolved into sophisticated systems that generate coherent essays, translate languages with near-human fluency, write functional code, and engage in nuanced conversations. At the center of this revolution stands Python, a programming language whose simplicity, versatility, and rich ecosystem have made it the de facto standard for artificial intelligence development. This book, LLMs with Python: The New Edition, serves as a comprehensive guide to mastering these powerful models through the lens of Python, bridging theoretical understanding with practical implementation. The emergence of large language models represents more than a technical achievement; it signals a paradigm shift in how humans interact with machines. Applications that once required extensive rule-based programming or specialized expertise can now be constructed with a few lines of Python code interfacing with a pre-trained model. A customer service chatbot that understands context across multiple turns of dialogue, a legal assistant that summarizes contracts while highlighting potential risks, or a creative tool that drafts marketing copy tailored to specific demographics-these are no longer futuristic visions but everyday realities built by developers using Python libraries such as Transformers, LangChain, and PyTorch. The accessibility of these tools has democratized artificial intelligence, enabling individuals and small teams to create solutions that rival those of large corporations. Understanding why Python has become the universal language for artificial intelligence requires examining its historical trajectory and technical advantages. Introduced in 1991 by Guido van Rossum, Python was designed with readability and ease of use as core principles. Its syntax, which emphasizes indentation over braces and favors expressive one-liners, allows developers to focus on solving problems rather than wrestling with language complexity. In the context of machine learning and artificial intelligence, this clarity becomes particularly valuable when working with complex mathematical concepts such as gradient descent, attention mechanisms, or loss functions. A single line of Python using the Hugging Face Transformers library can load a model with billions of parameters, whereas equivalent functionality in lower-level languages like C++ might require thousands of lines of code. The ecosystem surrounding Python has grown in parallel with the rise of large language models. The Python Package Index (PyPI) hosts over 400,000 packages, many specifically tailored for artificial intelligence development. Libraries such as NumPy and Pandas provide efficient data manipulation, Matplotlib and Seaborn enable visualization of training metrics, and scikit-learn offers classical machine learning algorithms that complement deep learning approaches. More importantly, the deep learning frameworks-TensorFlow, PyTorch, and JAX-all provide Python interfaces as their primary means of interaction. This convergence means that a developer learning to fine-tune a language model is simultaneously building proficiency in the broader Python data science stack, creating transferable skills across the artificial intelligence domain.