Book Image

Technical Writing for Software Developers

By : Chris Chinchilla
Book Image

Technical Writing for Software Developers

By: Chris Chinchilla

Overview of this book

Effective documentation is key to the success of products in remote software development teams, facilitating clear instructions that benefit the entire development team. Technical Writing for Software Developers lays a solid foundation of essential grammar, providing language tips and explaining how precise writing enhances documentation, and walks you through the fundamental types and styles of documentation. Starting with an exploration of the current state of the tech writing industry and its significance in both the software and hardware realms, you’ll master the building blocks of technical writing, exploring tooling choices and style guides, and create dynamic multimedia-laden documentation. This book equips you with valuable insights into the writing and feedback process to ensure continuous improvement. Additionally, you’ll take a peek at the emerging trends and technologies, including AI tools, shaping the future of technical writing. By the end of this technical writing book, you’ll have developed the expertise you need to tackle documentation requests effectively, armed with the knowledge of the best approach for documenting any topic, encompassing text, media elements, structure, and appropriate tools. The skills acquired will enable you to achieve seamless teamwork, enhanced project efficiency, and successful software development.
Table of Contents (12 chapters)

The principles of training and creating your own AI

So far, this chapter has mostly consisted of a lot of services you can look at and pay for to take advantage of some of the forward-looking ideas this chapter discussed. Throughout this book, I have tried to present options that give you as much flexibility and freedom as possible, preferably open source, free, and that give you the option to build upon them sustainably and stably. The current wave of AI tools has a lot of issues, which I cover throughout this chapter, but relevant to the current discussion is that the data sources of LLM-based AI tools are fundamentally different from what you might be used to. The code behind an AI tool might be open source, but this doesn’t necessarily mean the model that powers it is. The LLM data model behind an open source tool isn’t quite like a database you can potentially dig into and look at. Unless it’s connected to an application, you have specialized tools, or the...