Why We Use “As Little AI As Possible” When Building AI Powered Tools

So Sean and I gave this presentation a few weeks ago, three weeks ago, at a conference. And our title for this presentation then was as Little AI as Possible. You might be thinking, well, what do you mean? This is where it's a presentation about synthesis, AI search, why as Little AI as Possible? Over the last couple years, you might have been hearing these terms float around. Probabilistic versus deterministic, right? Traditionally, software that we built was deterministic, meaning we write code. When you press a button, X happens, the next time you press the button X happens again, you press it a third time, X happens again. It is if this, then that at its most simple kind of reductive form. And that is how we've been building software for a long, long time. AI has come in and it's different. You don't always get the same answer when you ask an AI a question, right? It's probabilistic. You can do things like ground it in knowledge to kind of have it draw from a certain set of facts, but it's going to potentially answer a question differently every single time. The facts shouldn't move around, but the answer will move around. And that nature, right, that kind of quality of AI is what's both maddening, but it's also what is so transformative and in terms of how it's changed the way that we can build technology. And so what our view is at Knowledge Architecture is we actually want to bring those two worlds together. We want to take the best of AI in terms of its ability to be creative, have intelligence, understand nuance and context, and pair it with deterministic technology that helps kind of constrain it into guardrails and focus on having AI do what it do best, but having kind of more traditional software approaches do what they do best. So that's why when I say we try and do as little AI as possible, we really believe in the ability of the AI to create great solutions, but we want to be very careful on when we're using it.