7 Lessons from building a small-scale AI application
ChatGPT heralded a seismic shift in software, and one that I felt compelled to understand. So, over the past year, I’ve been building an AI assistant for my past-CEO-self as a pedagogical exercise. It answers questions, gets status reports, and summarizes what’s going on. Reflecting on what I know now, here are my takeaways over the past year.
My biggest realization was that problems that I would have traditionally characterized as “scale up” problems occurred much earlier than I would have guessed. But at the same time, using AI tools such as Copilot, which I also wrote about, make a productivity difference too. It’s not slower, it’s not faster, it’s just different.
Unlike conventional programming, getting an AI to work the way you want is a stochastic process. Generally speaking, the process of “programming” an AI involves lots of experiments. In each experiment, you adjust different inputs and parameters and see if you can improve on what you did before.
As I went through the process, I started to develop an intuition as to the types of adjustments needed to address a given issue.
