Why There’s No ‘Right’ Way to Use AI

By Rhea Purohit / Learning Curve

Listen with Sir Michael Caine™ and 1,000+ voices

Listen for free

Length8m

About this audiobook

Was this newsletter forwarded to you?Sign upto get it in your inbox.If you prefer to listen to rather than read our essays, we’re live on ElevenLabs’s ElevenReader app.Download the appand subscribe to our feed to listen to audio versions voiced by AI.For a long time, I felt like an imposter inside Every.I’ve been a writer here for over a year—and I thought everyone on the team was “good” at AI…while I wasn’t.What do I mean by “good”? It goes beyond technical knowledge of LLMs, likechain of thoughtorfew-shot learning. Instead, they used AI with a natural fluency; an intuitive sense of its uses and limitations. I kept up with the zeitgeist and tried out new models, but I still fumbled to find that quiet competence.Until the fog finally lifted when I spent a Saturday in November getting familiar with Excelfunctions. I realized that you can learn the “right” way to use conventional softwares like Excel, but generative AI is inherently different—it’s less about searching for the “right” way, and more about defining what that means for you.The difference between conventional software and AII live in Spain, and perhaps theonlyboring thing about that is dealing with bureaucratic immigration processes. In November, one of these regulations required me to calculate the exact number of days I’d spent outside the country in the last four years. Tedious stuff, indeed. As I thumbed through my passport for entry stamps and scoured my email for flight tickets, I logged the information in an Excel sheet. I used simple functions to calculate the number of days in each trip I’d taken and add them up at the end.I wouldn’t call myself an expert in Excel, but I got comfortable with the basics of the software quickly. It was easy. I figured out which formula I needed, typed it in, and if I made a mistake, Excel would helpfully throw up an error in that cell—I saw #NULL! or #NAME! and knew I’d gotten something wrong. (I was dealing with a relatively simple task, and there are certainly far more complicated functions within Excel that I haven’t begun to learn about.)When you input a formula correctly in Excel, you get the right answer. If there's an error, Excel points it out. This clear distinction between right and wrong made me feel confident. I was certain I was using the application correctly. The direct feedback loop goes beyond just Excel to most types of conventional software—and it struck me how different this was from using an LLM.I use Claude while I write sometimes, to brainstorm ideas for a lede, or for feedback on a piece. The chatbot always returns coherent, polished answers, even to prompts riddled with typos and garbled context. There’s no clear way to know if I’m using AI the “right” way. The beauty of LLMs is also their curse—there is no one,trueway to get the most out of the technology. Add to that the possibility that the answers are objectively wrong because the models are prone tohallucinations. That nagging feeling I had about not being “good” at AI was about understanding the shades of gray it exists in.AI’s stochastic dimensionBecome apaid subscriber to Everyto unlock this piece and learn about:LLMs as "chainsaws disguised as kitchen knives"From learning rules to defining relationshipsPersonalization as the path to masteryUpgrade to paidClick hereto read the full postWant the full text of all articles in RSS?Become a subscriber, orlearn more.

Artificial Intelligence Psychological Journey Healing Exploration