Book Image

LaTeX Cookbook - Second Edition

By : Stefan Kottwitz
Book Image

LaTeX Cookbook - Second Edition

By: Stefan Kottwitz

Overview of this book

The second edition of LaTeX Cookbook offers improved and additional examples especially for users in science and academia, with a focus on new packages for creating graphics with LaTeX. This edition also features an additional chapter on ChatGPT use to improve content, streamline code, and automate tasks, thereby saving time. This book is a practical guide to utilizing the capabilities of modern document classes and exploring the functionalities of the newest LaTeX packages. Starting with familiar document types like articles, books, letters, posters, leaflets, and presentations, it contains detailed tutorials for refining text design, adjusting fonts, managing images, creating tables, and optimizing PDFs. It also covers elements such as the bibliography, glossary, and index. You’ll learn to create graphics directly within LaTeX, including diagrams and plots, and explore LaTeX’s application across various fields like mathematics, physics, chemistry, and computer science. The book’s website offers online compilable code, an example gallery, and supplementary information related to the book, including the author’s LaTeX forum, where you can get personal support. By the end of this book, you’ll have the skills to optimize productivity through practical demonstrations of effective LaTeX usage in diverse scenarios.
Table of Contents (16 chapters)

Using Artificial Intelligence with LaTeX

In recent years, there has been remarkable progress in artificial intelligence (AI), which refers to machine or software-simulated intelligence. AI involves processing extensive data and learning through logic, statistics, and algorithmic training.

Generative AI, in particular, can create text, images, and videos. This is highly useful for us, as LaTeX revolves around text, both regarding content and source code. Text generation involves using a large language model (LLM) trained on vast datasets. You can give it some input text, a so-called prompt, and it predicts the following words based on the statistical relationships it has learned. So, based on the LLM’s language expertise and training data, you may get an excellent answer to your question or a response that at least seems to fit somehow, as good as it can be.

An example of such an LLM system is ChatGPT (Chat Generative Pre-trained Transformer), a chatbot developed by OpenAI...