3 Simple Rules for Using my Large Language Model
When it comes to AI, it seems like the vast majority of people I talk to believe
large language models
(LLMs) are either going to surpass human intelligence any day now or are a
crypto-scale boondoggle with zero real-world utility. Few people seem to land
in-between.
Not a ton of nuance out there.
The truth is, there are tasks for which LLMs are already phenomenally helpful,
and tasks for which today's LLMs will invariably waste your time and energy.
I've been using ChatGPT, GitHub Copilot, and a dozen other generative AI tools
since they launched and I've had to learn the hard way—unlike with web search
engines, perhaps—that falling into the habit of immediately reaching for an LLM
every single time I'm stuck is a recipe for frustratingly inconsistent results.
As B.F. Skinner taught
us, if a tool is
tremendously valuable 30% of the time and utterly useless the other 70%, we'll
nevertheless keep coming back to it even if we know we're probably going to get
nothing out of it. Fortunately, I've been able to drastically increase my
success rate by developing a set of heuristics to determine whether an LLM is
the right tool for the job before I start typing into a chat window. They're
based on the grand unifying theory that language models produce fluent
bullshit, which makes them the right tool for the job when you desire fluent
output and don't mind inaccurate bullshit.
Generative AI is perhaps the fastest-moving innovation in the history of
computing, so It goes without saying that that everything I suggest here may be
very useful on June 9th, 2024, but will read as a total farce in the distant
future of November 30th, 2024. That said, if you've been sleeping on using LLMs
in your daily life up to this point and are looking to improve your mental model
of how to best relate to them (as opposed to one-off pro-tips on how to accomplish
specific tasks), I hope you'll find this post useful.
So here they are, three simple rules to live by.
You'll never guess what happens next…