The Fastest Way to Learn AI Is to Build Something With It

Ask ten people how to start learning AI, and you will get ten different directions to go. Newsletters, podcasts, a webinar someone swears by, a course that guarantees it’ll all click. Plenty of that is genuinely useful.

The trouble shows up the second you sit down to use it. You can explain what a context window is and still have no idea what to do when your agent returns confident nonsense on a Thursday afternoon. That kind of knowledge doesn’t transfer through a slide. You build your way into it, or you don’t get it at all.

Our team spends a lot of time in this gap, both in client work and in the rooms where people come to learn. A few patterns keep surfacing.

Start With a Problem You Already Have

“Find a problem you’re looking to solve,” said Sarah Bhatia, Director of AI Product Innovation at Slingshot. “Look for a pain point in your day or week, and then start exploring whether something in the AI space could solve it.”

A real problem gives you two things a tutorial never will: a finish line and a feedback loop. You know it works because the annoyance goes away. You know when it doesn’t because you’re still doing the annoying task by hand.

That framing also protects you from the biggest beginner trap: treating AI as a subject to study rather than a tool to point at something. Studying AI produces opinions. Pointing it at your Tuesday morning report produces skill.

Stef Hogan, Product Lead at Slingshot, builds on that with a second move. “Start getting your hands dirty and actually using it, but also find a group of people you’re comfortable with to talk through the hard parts and the confusing parts,” she said. “There’s a lot of power in building in public and building with community.”

Nobody Has This Figured Out, and That Helps

Software has always carried a myth about the solo operator. “But we’re facing a unique landscape now, on the front line of this technology, with the most pressure around using it,” Sarah said. “It’s been really important to find spaces where we can not only commiserate around the frustration, but find solutions together.”

The pace is what breaks the old model. Tech workers adapt to change for a living, but nobody has watched a technology shift this broadly this fast. Expertise decays in months. The person who solved your exact problem last week is more valuable than the documentation.

Stef named the takeaway she keeps leaving AI events with. “No one has this figured out,” she said. “At conferences about other areas of tech, people do have it figured out: Agile, project management, you walk away with something to work with. With AI, it’s the validation that it’s moving quickly, that it’s okay you don’t have it figured out. And here are some tools to keep problem-solving faster.”

That is not a consolation prize. Knowing the whole field is improvising frees you to improvise.

The Mindset Travels, the Tooling Expires

New agents ship weekly. Model capabilities jump in ways that make last quarter’s workflow look quaint. So people reasonably assume that learning AI means chasing the stack.

“We get really hung up on the tooling,” Stef said. “My setup is completely different than Sarah’s. You talk to someone else and think, ‘shoot, my setup is different, I should switch.’ You don’t have to do the same thing as another person. It’s more about the first principles of how you approach what you’re doing.”

The durable part sits underneath the tools. “Approaching a problem a certain way, breaking it down a certain way, the importance of context, the importance of understanding how to troubleshoot when things go wrong,” Stef said. “That stays the same.”

Tooling matters eventually. Once you know what you’re doing, preferences get sharp and specific, and the right tool for a task saves real hours. But you can’t develop that judgment by reading comparison charts; you develop it by breaking something and fixing it.

Adults Already Learn by Doing

There is a strange inversion in how we teach grown people.

“When we were in K-12, you’re mostly talked to the whole time,” Stef said. “You’re lectured, then you go do the worksheet. As adults, it’s completely flipped. You’re in a job, and it’s, ‘Here’s this thing, do it.’ It rewires our brain to learn by doing. So I don’t understand why conferences for adults aren’t do-and-learn.”

Every professional in the room has spent a decade or more learning by getting handed something unfamiliar and figuring it out. Then we put those same people in chairs for eight hours and ask them to retain material they won’t touch until next week, if at all.

Sarah pointed at what makes the difference between a good day and a wasted one. “Conferences can be good for being exposed to new technology and new solutions, but I want to know that going into it,” she said. “If I show up expecting to really learn something, I expect to walk away with something tangible.”

The fix is not complicated. Put the tool in their hands while the expert stands there. That reasoning shaped the AI workshop our team is co-hosting in Louisville this October: PROOF AI. Whitney Powell, Marketing Manager at Slingshot, helped build the format by working backward from the days that didn’t work. “Going to these conferences, it’s a lot of back-to-back packed lectures,” she said. “It’s hard to keep people’s attention throughout the day, and packing all that in can be very overwhelming. That was the first thing we decided to throw out.”

What replaced it is a day built around working sessions, where each track hands attendees something to build, and the people who build for a living stay in the room while they do it.

Watching Someone Troubleshoot Beats Watching Them Succeed

Polished AI demos teach almost nothing, because the interesting decisions already happened offstage.

Stef is deliberately doing the opposite in her PROOF AI session. “It’s going to be very real life, meaning it’s not going to work,” she said. “There’s going to be things I have to troubleshoot in front of people, and that’s the point of the topic: for people to see the way I approach the problems, and how it might be more advanced or different from how they would approach it, but how it gets to the outcome.”

That is the part nobody writes down: the recovery. The moment you realize the model doesn’t have the context that you assumed it had, and you decide what to change.

Sarah is running at the same idea from the strategy side, and she thinks most people are still stuck in the first gear of AI use. “Up to this point, most people have engaged with AI from an efficiency standpoint, trying to speed up their work and do more in less time,” she said. “Speed is the least interesting way to use these tools. Phase two is about quality. How do we use these tools to go deeper, to create stronger output, by capitalizing on our unique human insight?”

You Should Leave With Something That Works

The best measure of a learning day is what still exists the next morning.

Whitney is building her session around exactly that. “Somebody is going to bring a specific task they’re dealing with weekly or daily, and we’ll help them craft it into a working skill in Claude, ChatGPT, Copilot, or whatever tool they use,” she said. “Then we give them the framework to go do it again with another repeatable task.”

The same logic shaped how the PROOF AI Workshop ends. Instead of a final keynote, the day finishes with attendees showing each other what they made, plus a wall where people post what they built as they go. “These people are going to be there all day building stuff they should be proud of,” Whitney said. “So everybody gets the opportunity to share what they were able to build. It gives people insight into how they could go back and do that with something else at work.”

Sarah described where the PROOF AI format came from, and the analogy lands. “It came about organically from a bunch of people who’ve gone to AI conferences and wanted to put one together that we’d actually enjoy,” she said. “It’s like being the last of your friends to get married, so you put together a wedding with all the stuff you liked and leave out the stuff you didn’t.”

What Actually Sticks

The through line is friction. Not the bad kind that wastes your afternoon, but the productive kind that comes from choosing a real problem, getting stuck on it in front of other people, and working out what to do next.

Reading about AI gives you vocabulary. Building with it gives you judgment, and judgment is the only thing that survives the next model release. Your problem is the curriculum. The people around you are the support system. The thing you get working by the end of the day is the proof.

So the question is not whether you understand AI yet; it’s what you’ll make it do before next Friday.

Slingshot