This is a recording of our AI book club discussion of <a href='https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961'>Hands-On Large Language Models: Language Understanding and Generation</a> by Jay Alammar and Maarten Grootendorst, held Oct 19, 2025. The book differs from other books in the series in that it's a more technical exploration of how LLMs work, without any ethics discussions. It's less narrative and more engineering-oriented. Our discussion focuses on understanding of conceptual details and whether, to use an analogy, understanding the plane's engine helps pilots fly the airplane better.

In this podcast episode, Fabrizio Ferri Benedetti and I chat with guest Anandi Knuppel about MCP servers and the role that technical writers can play in shaping AI capabilities and outcomes. Anandi shares insights on how writers can optimize documentation for LLM performance and expands on opportunities to collaborate with developers around AI tools. Our discussion also touches on ways to automate style consistency in docs, and the future directions of technical writing given the abundance of AI tools, MCP servers, and the central role that language plays in it all.

In this conversational podcast, Fabrizio Ferri Benedetti (<a href='https://passo.uno'>Passo.uno</a>) and I talk about the impact of AI on the technical writing profession. We tackle the anxiety, seen and felt almost everywhere, but especially on Reddit, within the community about job security and analyze the evolution of the technical writer's role into a more strategic context curator or content director. We also cover practical applications of AI, such as using agents markdown files to guide language models (with style overrides or API reference contexts), and the role documentation plays in improving AI's outputs (Fabri's phrase <i>AI must RTFM</i>).

This is a recording of the AI Book Club discussion about Karen Hao's <i>Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI</i>. The discussion is an informal, casual discussion with about half a dozen people online through Google Meet. You can also read a transcript and other details about the book here.

This is a recording of our <a href='/ai-book-club'>AI Book Club</a> session discussing Kai-Fu Lee's <a href='https://www.amazon.com/AI-Superpowers-China-Silicon-Valley/dp/132854639X'><i>AI Superpowers: China, Silicon Valley, and the New World Order</i></a>. There are 4 people in this book club discussion, and our conversation focuses on the emerging AI duopoly between the US and China. We share our own US-centric blind spots and weigh the political and cultural implications of China potentially winning the AI race. We also talk about Kai-Fu Lee's prediction of mass job displacement and his proposed <i>social investment stipend</i>, questioning both its feasibility and its potential drawbacks. The discussion also explores how our own professional roles, particularly in tech comm, might evolve to manage and direct AI rather than be replaced by it, and how surprisingly relevant the book feels today despite being published in 2018.

In this podcast, I chat with Fabrice Lacroix, founder of Fluid Topics, about the evolution of technical communication. Fabrice describes the industry's progression from (1) delivering static, monolithic PDFs to (2) using Content Delivery Platforms (CDPs) that provide dynamic, topic-based information directly to users to (3) developing content not just for human consumption, but for AI agents that will use this knowledge to automate complex tasks and workflows.

This is a recording of our <a href='/ai-book-club'>AI Book Club</a> session discussing Ray Kurzweil's <a href='https://www.amazon.com/Singularity-Nearer-When-We-Merge/dp/1847928307'><i>The Singularity is Nearer podcast: When We Merge With AI</i></a>. You can watch the recording on YouTube, listen to the audio file, read some summary notes, browse discussion questions, and even listen to a NotebookLM podcast (based on the summary). There are 5 people in this book club discussion, and we focus a lot on the topics of acceleration, especially as we see it happening in the workplace. We also weigh in on Kurzweil's techno utopianism and how persuaded we are by the arguments about AGI landing in 2029, the likelihood of machine and biology merging (through nanobots), and more.

This post describes the key arguments and themes in <i><a href='https://www.amazon.com/Coming-Wave-Power-Our-Future/dp/0593593979'>The Coming Wave: AI, Power, and Our Future</a></i>, by Mustafa Suleyman, for the <a href='/ai-book-club'>AI Book Club: A Human in the Loop</a>. This post not only breaks down the logic but also jumps off into some themes (beyond the book) that might be more tech-writer relevant, such as potential future job titles, areas of focus for tech writers to thrive now, questions for discussion, and more. It also contains the book club recording.

This post has notes and questions for discussion for <em><a href='https://www.amazon.com/More-Than-Words-Think-Writing/dp/1541605500'>More Than Words: How to Think About Writing in the Age of AI</a></em>, published in February 2025 by Jonathan Warner. Warner's book, which explores what we lose when we outsource writing to AI, is the first book in the <a href='https://idratherbewriting.com/ai-book-club/'>AI Book Club: A Human in the Loop</a>.

I gave a presentation to the <a href='https://www.meetup.com/write-the-docs-australia/'>Write the Docs Australia group</a> on using AI to write release notes using file diffs, on Feb 16, 2025. Here's the recording, presentation description, and transcript.

It's that time of year again when we take to analyzing trends. If you know me, you're probably gearing up for a load of AI-optimistic predictions because, as I've noted in previous posts like <a href='/blog/unpacking-issues-from-ai-porter-benedetti-posts'>Unpacking the issues from AI</a>, I'm an AI optimist. However, my AI optimism isn't based on hype or the current tech zeitgeist. Rather, I'm an AI optimist because my daily experiences using AI for technical documentation, especially API docs, throughout 2024 has shown it to be invaluable.

This podcast explores GenAI in technical documentation scenarios, highlighting the AI features and capabilities provided in <a href='https://document360.com'>Document360</a>. I talk with <a href='https://www.linkedin.com/in/saravanamv'>Saravana Kumar</a>, CEO of Kovai.co, which makes <a href='https://document360.com'>Document360</a>, about how AI is changing search functionality and reducing support costs in knowledge bases. We discuss practical applications of AI for technical writers, including automated tagging, SEO optimization, glossary creation, and more. Saravana shares about AI agent workflows, conversational search experiences, automating screenshot captures, and much more.

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.

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.

In this podcast, I chat with <a href='https://www.linkedin.com/in/docsbydesign/'>Bob Watson</a> about an upcoming <a href='https://www.pce.uw.edu/specializations/api-documentation'>API documentation course</a> he'll be teaching at the <a href='https://www.pce.uw.edu/'>University of Washington</a>. Bob has extensive experience working as an API technical writer at big tech companies like Microsoft, Amazon, and Google. The UW reached out to Bob to develop this new course offering. The 14-week evening course will cover fundamentals like understanding developer behaviors, working with various types of APIs, publishing workflows, as well as hands-on practice. A key component is having students create API documentation portfolios they can use to demonstrate their skills.