One of the most common questions people ask my personal AI, Ask LukeW, is "how did you build this?" While I've written a lot about the high level architecture and product design details of the service, I never published a more technical overview. Doing so highlighted enough interesting generative publishing ideas that I decided to share a bit about the process.
First of all, Ask LukeW makes use of the thousands of articles I've written over the years to answer people's questions about digital product design. Yes, that's a lot of writing but it's not enough to capture all the things I've learned over the past 30 years. Which means sometimes people Ask LukeW questions that I can answer but haven't written about.
In the admin system I built for Ask LukeW, I can not only see the questions that don't get answered well but I can also add content to answer them better in the future. Over the last two years, I've added about 500 answers and thereby expanded the corpus Ask LukeW can respond from by a lot. So the next time similar questions get asked, people aren't left without answers.
That process is an interesting part of generative publishing that I've written about before but it's also how I know that people regularly ask how I built Ask LukeW. they want technical details: what frameworks, what models, what services. I never wrote this up because I'm not that technical and several great engineers helped me build Ask LukeW. As a result, I didn't think I'd do a great job detailing the technical aspect of things.
But one day it occurred to me I could use our AI for code company, Augment Code, which has a deep contextual understanding of codebases to help me write up how Ask LukeW works. I opened the codebase in VS Code and asked Augment the questions people asked me: "how does the feature work?" "what is the codebase?" "what is the tech stack?" and got great detailed responses.
Augment, however, doesn't answer questions the way I do. So I took Augment's detailed technical replies and dropped them into another one of our companies, Bench. A while back I had Bench read a lot of my blog posts and create a prompt that writes articles the way I would. I've saved this prompt in Bench's agent library and can apply it anytime I want it to write like I would.
Once I had Augment's technical details of how Ask LukeW worked written the way I'd explain them by Bench, I took the results and added them as saved answers to the Ask LukeW corpus. Now anytime someone asks these kinds of questions, they get much more detailed technical answers. In fact, this worked so well that I also asked Augment to write up the overall tech stack for my Website and went through the same process.
I for one, found this a really enlightening look at where generative publishing is now. I can see what kinds of information I should be publishing by looking at the questions people ask my personal AI but don't get good answers for. I can use an AI for coding tool to turn code into prose. I can use an agentic workspace to rewrite that prose the way I would because I taught it to write like me. And finally I can feed that content back into my overall corpus so it's available for any similar questions people ask in the future.
That doesn't look like the publishing of old to me. Of course, it's split between multiple tools, requires me know what each one can do, and a host of other issues. We're still early but it's exciting.