The following is a guest post by Bonnie Denham. In this post, she responds to one of the most common questions people ask about technical writing careers: what does a typical day look like for a technical writer? I find it interesting to see how the day-to-day tasks differ not only by company and industry, but by software, specializations, and company environments.
I've always been keen on making goals, but only a few goals ever stick and become normalized as part of my daily activities. However, this past month, I've come across the idea of habit stacking. Habit stacking is the practice of adding a new habit onto an existing habit or routine, using it as the trigger for doing the new habit. Based on my recent experiences, it seems to be working.
This tutorial will help you understand task decomposition by guiding you through the process of creating a complex tree diagram that's too sophisticated for an AI tool to create at once. Whether you're creating tree diagrams or not, it doesn't matter. This is just an example of how to break down complex information into smaller chunks and pass it into AI.
One of the advantages of recent Gen AI updates is the massive token input context. When you can pass in an entire set of documentation as an input, you have a much stronger possibility for powerful prompts. In this tutorial, I share some quality-control prompts you can use that deal with entire doc sets as inputs, as well as explain some of the challenges in passing in an entire doc set.
You can use AI prompts when creating release notes for APIs by leveraging file diffs from regenerated reference documentation. The file diffs from version control tools provide a reliable, precise information source about what's changed in the release.
In this tutorial, you'll learn how to use AI to populate documentation templates with the source material you've gathered. For example, API overviews often follow a highly structured template. This technique can be a quick way to get an initial draft of documentation, which you can then edit and review with SMEs.
One of the most successful strategies for using AI is to pass in an abundance of source material that can augument and inform the AI's responses. In this tutorial, I cover strategies for gathering this material, including what types of documents to look for, optimal ordering, pitfalls to look out for such as outdated or slanted information, and more.
For AI tools to generate accurate information for documentation you're writing, you need to pass in source material. This usually means meeting with engineers and product managers to gather information about the product. In this tutorial, I share prompts for turning those meeting transcriptions into organized, readable meeting summaries. These cleaned up summaries can then function as input context for documentation-oriented prompts.
Just as we need regular physical training to keep from physical decline, we also need regular training in our daily work. In this post, I reflect on the parallels between physical training and work training, resolving to find a regular rhythm for daily reflection and experimentation about work issues.
One of the advantages of recent Gen AI updates is the massive token input context. When you can pass in an entire set of documentation as an input, you have a much stronger possibility for powerful prompts. In these prompts, the reference docs can serve as a key source of truth. User guide content and drift out of date, but a freshly generated reference doc should be accurate to the code base, for the most part. From this source of truth, you can do all sorts of things, such as identify outdated content in the user guide, see what's new between outputs, get links in your release notes, and more. In this article, I share 8 quality control prompts you can use when passing in your entire reference docs.
In this essay, I explore the idea of seeing the unseen aspects of things. I discuss several authors on this topic: Rob Walker, an art critic; Viktor Shklovsky, Russian formalist literary critic; and Robert Pirsig, author of Zen and the Art of Motorcycle Maintenance. My main point is to avoid predictable, conditioned thought by pausing to ask questions about our experiences and the environment around us. In a world where prediction algorithms constantly direct us toward the most likely next word, pushing back and embracing creative ways of seeing and interpreting the world can inject new ideas and perspectives in ways that rejuvenate us.
My previous prompt engineering technique focused on creating release notes using file diffs. In this article, I explain how to use AI to link all the code elements, often referenced in release notes and other documentation, to their appropriate reference page. The technique basically involves providing your reference documentation in HTML form along with instructions to link all the code elements in Markdown syntax.
The following are interesting reads or listens related to tech comm. Topics include podcasts on RAG techniques for AI content development, OpenAPI reference guides, dead-end counterarguments, Lavacon in Portland, and AI cautiousness.
![[Podcast] Uncovering and communicating the value of your tech comm teams' work, with Keren Brown](https://s3.us-west-1.wasabisys.com/idbwmedia.com/images/kerenbrownpodcastthumb.png)
In this podcast episode, I talk with Keren Brown, VP of Marketing and Value at Zoomin Software, about strategies for technical writers to demonstrate their value within their organizations, especially in light of recent layoffs in the tech industry. We discuss aligning documentation work with high-priority initiatives, quantifying the impact of technical writing, and making this work visible to executive leaders. Keren also shares insights on the changing landscape of technical writing skills in the age of AI and the role of translation in modern documentation workflows. Overall, this podcast will show you how to establish yourself as a highly valuable resource within your company.
You can use AI prompts when creating biweekly release notes for APIs by leveraging file diffs from regenerated reference documentation. The file diffs from version control tools provide a reliable, precise information source about what's changed in the release. I also include a detailed prompt for using AI to analyze file diffs and streamline the release note creation process.
