In the first part of this series, I described the most important lesson I learned while building a local AI storyteller:
A good LLM application is not a clever prompt. It is software architecture around a probabilistic component.
That sounds reassuringly architectural.
It also leaves one small question:
What exactly are we building around?
As software engineers, we are constantly challenged to explain what we do and how we do it. This communication challenge becomes even more pronounced when we talk to non-technical stakeholders. Now that AI is enabling us to produce more than ever before, this problem has become even greater. But how do we effectively convey complex technical concepts to a non-technical audience?
It was two o’clock in the morning before I noticed the time.
Earlier that evening I had been building an application.
I had written all the work in a backlog.md, a neat list of stories, and I let the AI pick them up one by one.
It would finish a story, ask me to verify everything, and then close with a simple question: shall I pick up the next task?
And every single time, that question was the trigger. It would be a shame to stop now. Just this one more, and then I’ll go to bed.
