GeoAI Insights

Interacting with AI

Interacting with generative AI systems often feels like interacting with humans. But, of course, they are not human. Here we reflect on how we should interact with them, given how they actually work. We assume the reader is familiar with the ideas in our two Generative AI Basics posts. Note that while we're tackling how to be thoughtful and critical in our interactions with these systems, we're approaching this through the lens of how their internal mechanics can guide that critical use. There are a variety of other critical lenses one could use, especially around the myriad of ethical issues they raise. While we encourage the reader to pursue those as well, we're taking a more specific and narrow focus here. We'll start with a step back and reflect on how interactions with AI systems play out if we simply rely on past experiences and expectations, rather than bringing an appropriate critical lens to bear. More

Generative AI Basics - Part 1

This series of posts is intended to introduce the reader to the mechanics of how generative AI tools (e.g. ChatGPT, Gemini, Claude) actually work under the hood. The goal is to provide enough detail to allow the reader to build a reasonably accurate conceptual understanding so that when they are making decisions about how to use these tools, those decisions can be informed by how they actually work. More

Generative AI Basics - Part 2

If you missed the first part that tackles how LLM's work you'll want to review that before we jump into this discussion about all the bits surrounding a raw LLM.

The Harness Controls the LLM

So we've seen how (with a little magic) a string of words fed into an LLM can lead to a prediction for the possible next word. How does this relate to GenAI tools we've actually used? The key point is that there is always another layer of software sitting between you and the LLM. While it goes by different names (and can take very different forms), this is often referred to as the 'harness'. More