Og Ramos

Prompt engineering won't be a job. It will be like knowing how to Google.

Everybody's going to be a prompt engineer in the future. Like, I don't think it's a role. I think everybody should be a prompt engineer. Right. Because it's kind of like learning how to Google, right. It's the new learning how to Google.

AI knows the language. It still doesn't know how to think like a programmer.

Coding is not just learning how to use a programming language and putting like writing, writing it out. And that's what the AI is doing. It knows the language and it writes it out. The important piece here is knowing the patterns, knowing best practices, knowing how things work, knowing how to create architectures, knowing how to create patterns in those architectures and having that way of thinking.

Knowing how to code and knowing how to program are two different things.

Programmer is not the language. A programmer is not the code. And if that is the programmer, that is in my opinion, okay, is not somebody that knows how to program. They know how to code, which is different, right?

Starting with AI? Hire the plumbers before the roofers.

You just answered the question. Who do you need? Data engineer, right? Because you're building the foundation. That's what you bring the plumbers in first to do the base of the house, right? You don't bring the roofers, which are the data scientists and the ML folks, right?

Your future AI assistant is a guild apprentice that you review.

back in the day when we had guilds and you had like apprentice, it's kind of the same thing. Your apprentice is helping you out to do the tasks that maybe you don't want to put too much time or you're not in this mind space to do that, but they could do it and you just review it as the person that knows what they're doing.

The flood of AI apps looks a lot like Atari's E.T. moment.

Just look at the video game industry, man. Like, no, seriously. Like if you look at how it corrects itself, I don't know how much you know about video game history, but Atari went through something like this. They were just creating games that were like the, the whole like Atari ET game.

Even as an employee, you are a business of one.

You are a business always like, especially in our system, right? You, you, when you go to, as an employee, you are a business, you have a contract between a company and yourself. At the end of the day, you are operating as a business.

Trust in AI shouldn't be blind. It needs consequences for bad actors.

I do not believe in blind trust, but that's just me. I don't think we should just take things for granted. Or if somebody tells you something is just because they say it is, I always am very critical of that.

Most people can't answer this one SQL interview question, and it matters more than syntax.

the interview question where I ask, do you know how a SQL clause is executed? And not a lot of people know how to answer that question. They think the machine just reads from the select all the way down, and that's not how it reads it. It goes to, it kind of hops around because it's trying to give you the smallest data set the fastest way possible, right?

Putting a dashboard or an AI on bad data is just makeup on garbage.

everybody was like, well, we just get Tableau or we just do Power BI or Click or whatever. And it's going to work. I'm like, no, all you're doing is grabbing garbage. And they're like, yeah, yeah, garbage in, garbage out. I'm like, no, buddy, you're going from garbage in to garbage. You know, and it's, you know, you're just slapping makeup on top of it. And it's still bad data, right?

When the person who knew everything leaves, an LLM can keep what they knew.

You don't have to go and talk to so-and-so in this department and then go to somewhere else because that, oh, then that person left. You know, all of that should be centralized. Tribal knowledge, operational knowledge, all of that information fed into an LLM.

After the lakehouse, the next shift is leaving the search bar.

I think it's more of a, we're moving away from the search bar. That's what, the way I see it. Not from, from those patterns, more from the search bar. Instead of going to Google, I go more into an LLM to ask, right?

Data engineers and data scientists use the same hammer to build different things.

i'm trying to find like an analogy so carpenter and a builder they both use a hammer but they're doing a chair the other one's building a house

Data engineer is not just someone dragging boxes around.

people just spin up and say whatever they want oh yeah that person's a data engineer and all they're doing is i don't know like drag and dropping some boxes and and that's it and etl well you know it's a little bit more complex than that right