How we engage with LLMs

It seems popular to write about generative AI and large language models (aka LLMs) these days. There are a variety of ways in which people make sense out of this space and the whole phenomenon of “artificial intelligence” – I use double-quotes here, because the term has gotten quite blurry semantically.

I’ve been looking for a way to make sense of all of these bubbling insights, and here’s a sketch of a framework that is based on the Adult Development Theory (ADT). The framework presumes that we engage with LLMs from different parts of our whole Selves, with some parts being at earlier stages of development and some parts at the later. I call these parts “Minds”, since to us, they feel like our own minds, each with its own level of complexity and attributes. They change rapidly within us, often without us noticing.

These minds are loosely based on the ADT stages: the earliest and least complex Opportunist Mind, the glue-of-society Socialized Mind, the make-things-work Expert Mind, and the introspective Achiever Mind.

🥇The Opportunist Mind

When we engage with an LLM with an Opportunist Mind, we are mostly interested in poking at it and figuring out where its weaknesses and strengths lie. We are trying to trick it, to reveal its secrets, be that initial prompts or biases. From this stance, we just want to figure out what it’s made of and how we could potentially exploit it. Twitter is abuzz with individuals making LLMs act in ways that are beneficial to illustrating their arguments. All of those are symptoms of the Opportunist Mind approach to this particular technology.

There’s nothing wrong with engaging an LLM in this way. After all, vigorous product testing makes for a better product. Just beware that an Opportunist Mind perch has a very limited view, and the quality of insights gained from it is generally low. I typically steer clear from expert analyses engaging with LLMs from this mind. Those might as well be generated by LLMs themselves.

👥The Socialized Mind

When the LLM becomes our DM buddy or a game playing partner, we are engaging with an LLM with a Socialized Mind. When I do that, there’s often a threshold moment when I start seeing an LLM as another human being, with thoughts and wishes. I find myself falling into habits of human relationship-building, with all of the rules and ceremonies of socializing. If you ever find yourself trying to “be nice” to an LLM chat bot, it’s probably your Socialized Mind talking.

At the core of this stance is — consciously or subconsciously — constructing a mental model of an LLM as that of a person. This kind of mental model is not unique to the Socialized Mind, but when engaging with this mind, we want to relate to this perception of a human, to build a connection with it.

This can be wonderful when held lightly. Pouring our hearts to a good listener convincingly played by an LLM can be rather satisfying. However, if we forget that our mental model is an illusion, we get into all sorts of trouble. Nowadays, LLMs are pretty good at pretending to be human, and the illusion of a human-like individual behind the words can be hard to shake off. And so we become vulnerable to the traps of “is it conscious/alive or not?” conversations. Any press publication or expert analysis in this vein is only mildly interesting to me, since the perch of the Socialized Mind is not much higher than that of the Opportunist Mind, and precludes seeing the larger picture.

🧰The Expert Mind

Our Expert Mind engages with an LLM at a utilitarian level. What can I get out of this thing? Can I figure out how the gears click on the inside — and then make it do my bidding? A very common signal of us engaging LLMs with our Expert Mind is asking for JSON output. When that’s the case, it is very likely we see the LLM as a cog in some larger machine of making. We spend a lot of time making the cog behave just right – and are upset when it doesn’t. A delightful example that I recently stumbled into is the AI Functions: a way to make an LLM pretend to execute a pretend function (specified only as input/output and a rough description of what it should do) and return its result.

Expert Minds are tinkerers – they produce actual prototypes of things other people can try and get inspired to do more tinkering. For this reason, I see Expert Mind engagements as the fertile ground for dandelion-like exploration of new idea spaces. Because they produce artifacts, I am very interested in observing Expert Mind engagements. These usually come as links to tiny Github repos and tweets of screen captures. They are the probes that map out the yet-unseen and shifting landscape, serving as data for broader insights.

📝The Achiever Mind

I wanted to finish my little story here, but there’s something very interesting in what looks like a potential Achiever Mind engagement. This kind of engagement includes the tinkering spirit of the Expert Mind and enriches it with the mental modeling of the Socialized Mind, transcending both into something more.

When we approach LLMs with the Achiever Mind, we recognize that the nature of this weird epistemological tangle created by an LLM creates opportunities that we can’t even properly frame yet. We can get even more interesting outcomes than the direct instruction-to-JSON approach of our Expert Mind engagement by considering this tangle and poking at it.

The ReAct paper shone the light at this kind of engagement for me. It revealed that, in addition to direct “do this, do that” requests, LLMs are capable of something that looks like metacognition: the ability to analyze the request and come up with a list of steps to satisfy the request. This discovery took someone looking at the same thing that everyone was looking at, and then carefully reframing what they are seeing into something entirely different.

Reframing is Achiever Mind’s superpower, and it comes in handy in wild new spaces like LLM applications. Metaphorically, if Expert Mind engagements explore the room in the house, Achiever Mind engagements find and unlock doors to new rooms. The unlocking of the room done by ReAct paper allowed a whole bunch of useful artifacts, from LangChain to Fixie to ChatGPT plugins to emerge. 

This story feels a bit incomplete, but has been useful for me to write down. I needed a way to clarify why I intuitively gravitate toward some bits of insight in the wild more than others. This framework helped me see that. I hope it does the same for you.

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