Top 5 ChatGPT Use Cases for Professionals!

If you're watching this, you're probably tired of generic ChatGPT tutorials. So let's go over five ways professionals like you can actually use ChatGPT to get stuff done more effectively. Let's get started, kicking off with use case number one: context cheating.

By now, we all know the more context we share with AI tools, the better the output. For example, "assume the role of a copywriter" will perform worse than "assume the role of a copywriter with over 20 years of experience writing compelling sales copy. " The problem is, we're kind of lazy, and thinking of relevant context to include every single time is a lot of work.

So here's an embarrassingly simple trick: just copy and paste relevant context from existing sources. For example, top consulting firms use the SCQA framework to structure their presentations. Instead of us explaining what SCQA is to ChatGPT, go to Google, type in "SCQA framework," click into a link, copy an existing explanation, and paste that back into your prompt.

By the same logic, if you're writing sales copy, use the PAS (Problem-Agitate-Solution) framework. If you're preparing for performance reviews, use the SMART goal-setting framework. And this tip isn't just limited to frameworks.

I've asked ChatGPT to rewrite articles in the voice of a Google blog post by simply copying and pasting one of their articles for the keyword. Similarly, you can ask ChatGPT to write in the voice of the Wall Street Journal, New York Times, Reuters, Fox News, etc. It's slightly creepy, but you can also do this for authority figures.

One of my favorite authors is Lee Child, so I literally Googled "Lee Child style of writing," and I actually saved his writing style in my text expander. That way, when I ask ChatGPT to write in the voice of Lee Child, I can easily provide additional context by pasting his writing style. Pro tip: If you want to provide an entire web page as context, you can paste the URL directly instead of copy and pasting all the text with it.

However, I have to say this works much better in Google Gemini than ChatGPT. Speaking of Gemini, if you use Google Workspace tools, you might want to sign up for my newsletter to receive an insanely actionable tip every week. Link down below.

Use case number two: convert with intention. Diving right into an example, let's say we're preparing a slide deck and we come across this comprehensive 128-page industry report on the state of fashion by McKinsey. There's no way we're going through this whole thing, so let's bring in ChatGPT.

I won't read this entire prompt; I'll leave a link to this down below. But essentially, I asked ChatGPT to generate a structured outline using the attached PDF report. Instead of a basic request like asking for a summary, we're also giving ChatGPT a clear objective: prepare a business plan for traditional fast fashion companies like Zara to compete against rapidly growing new entrants like Shein and Temu.

This ensures the outline isn't generic; it reflects our specific needs and goals. Although this seems like such a minor thing, I can't overstate how much time this technique has saved me. We instantly turned a dense PDF report into something we can use immediately: a presentation outline with headers and bullet points that support our hypothesis.

And this was all possible because we converted with intention. Pro tip: We can even follow up and ask ChatGPT which pages from the original report it's referencing in its answer, and we're literally told which pages to refer to if we want to dive deeper or fact-check its output. But even with AI assistance, managing complex projects and information overload can be a real challenge in the workplace, and that's where today's sponsor, Coursera, comes in.

The Google Project Management certification is one of Coursera's most enrolled programs, where experienced project managers from Google teach us how to organize our work, prioritize tasks, and collaborate effectively, all while using industry-leading tools and methodologies. To be transparent, I'm mainly self-taught when it comes to project management since this certification didn't exist back in the day. But after enrolling, I really like the structured curriculum and the real-life examples they give.

So if you're ready to take your productivity and career to the next level, check out the link down below, and thank you, Coursera, for sponsoring this portion of the video. ChatGPT use case number three: answer leveling. Most of us know that ChatGPT can be awesome for brainstorming new ideas, but without a framework, the results can be hit or miss.

For example, one day it's giving you brilliant marketing campaign ideas, and the next it's telling you, "Hey, have you thought about making short-form videos? " Luckily, there's a way to fix this inconsistency, and the technique is called answer leveling. Credit goes to Ali Miller on LinkedIn for teaching me this.

Diving into one of my examples from work, I'm a product marketing manager in tech, and I want fresh ideas to engage with my B2B customers. So I tell ChatGPT a level one campaign would be email marketing, which is boring but safe. A level two campaign would be an offline or online event, which is higher production but nothing crazy.

The magic happens when I ask for five level three ideas based on my level one and level two examples, and the answers are really impressive: tailored audits with live consultations, access to a growth program that's probably harder to do, client success stories and case studies (which makes a lot of sense), a growth academy (so a series of online webinars—that's definitely doable), and hyper-localized campaign insights for SMBs. So all of these ideas are more interesting than just, you know, email marketing and standalone events. Or, let's say as.

. . A product marketer, I'm trying to uncover a deep user insight.

I ask GPT, "Hey, what are the pain points my target audience is facing? " After it answers, I say, "Hey, those are level one pain points. Now give me level two.

" After it answers the level two pain points, I say, "Now give me level three deep-seated fears and pain points. " Obviously, I can keep going with the levels, but I found level three to be the sweet spot between usable and straight-up crazy ideas. Next up, one of my favorite use cases: seamless data extraction using GPT.

I put together an open roles database for my friends who recently got laid off. What I did was, step one, I went to a career site—in this case, Google—and filtered for location and job title. Command (or Control) A to select all, Command (or Control) C to copy all the text on this page.

Then, going back to ChatGPT, I use this prompt: "Your task is to create and populate a table with the following columns: 1, 2, 3, 4, 5, 6. " I've included many instructions, like leaving this column blank because you're not going to have the application link, and generating a few keywords for that specific role—up to five keywords. Then I end the prompt with, "Here are the open roles," and I paste all the text from the careers page.

ChatGPT will then output a table looking like this, and we can download this table in a CSV file. Then we can import that CSV file into Google Sheets, Excel, or Notion. In this case, I'll use Notion: double-click and import.

After this has been imported, we can make some slight adjustments; for example, instead of text for keywords, we can change this to multi-select right here. We can then clean this table up or the keywords up a little bit as needed. But as you can probably tell, this has already saved us from a bunch of manual work.

And just to prove to you I'm not bullshitting, this is the public page I created and shared with my LinkedIn network. Honestly, if I didn't have ChatGPT to help me with this project, I probably would have still done it because I'm such an angel. But taking a step back, creating a table is just one example of what's possible.

The real takeaway here is using ChatGPT to extract and transform unusable data into something we can actually work with. Use Case Number Five: uncover actionable insights. Diving right into another example, I tell ChatGPT, "I just created a paid wallpaper app, and for some weird reason, I'm receiving negative feedback.

By the way, if anyone from Marquez's team is watching this, I'm just kidding; I love your work. I'm just capitalizing on the hype here. " I ask it to recommend three categories to bucket the feedback and attach a spreadsheet with the list of qualitative feedback.

After ChatGPT replies with the three feedback categories, we can follow up with, "Now add two more columns: feedback type by matching the feedback to the appropriate category and the team responsible for taking action on that feedback. " If we were to expand this, we'll see that ChatGPT has indeed added two columns to the spreadsheet we provided. We can wrap up this conversation with, "Okay, now your task is to analyze the feedback and highlight the top three insights.

" Then, since I have limited time and resources, I apply the 80/20 rule and recommend three action items for the team: be as detailed as possible, share specific examples, and let's think step by step. This is pretty amazing. In the output, ChatGPT ties a specific example to the insight it came up with under step one, and in step two, it recommends really detailed and actionable next steps—starting with why we should make this change, how we're going to make the change, an example of the change in action, and the impact the change will have.

So, I'm not an app developer, but some of this is pretty good. For example, add a feature where paid users can vote on upcoming wallpaper packs—that's not bad, right? And here, introduce a 7-day or 30-day free trial for the paid version—that's also a good idea, right?

Anyways, although this is an oversimplified example, just think about how much time it would normally take to sift through hundreds, if not thousands, of rows of feedback, and how much more time it would take to come up with actionable recommendations. Pro tip: and this is something I do all the time at work. Now, I usually take it a step further and ask ChatGPT for ways to visualize the data to my stakeholders.

I can even take it a step further most of the time by asking ChatGPT to create that chart or visualization so I can either copy and paste this into a slide or build on top of it immediately. I hope this was helpful, and if you want to see even more real-life use cases, let me know because I have a bunch more. In the meantime, check out my AI for productivity playlist.

See you on the next video, and have a great one!