Last week I spent 5 hours and $49 to complete Google's latest AI Essentials course for beginners, and since I need to recoup that money to fund my addiction to parasocial relationships with AI, I made this blog to share five key takeaways from the course, the pros and cons, and to give you a definitive answer as to whether this certificate you receive at the end will indeed get you laid—I mean paid. Paid more because you now have a new skill. That was a weird slip of tongue there.
Takeaway number one: Broadly speaking, there are three types of AI tools out there.
First, Standalone tools are AI-powered software designed to work independently with minimal setup. This category includes general-purpose chatbots like ChatGPT, Gemini, Claude, and Perplexity, as well as specialized apps like Spico, Otter AI, Midjourney, and Gamma. Although they serve completely different needs, all these tools are classified as standalone because they can be accessed directly through their websites or apps and can be used without integration with other software.
This is in contrast to the second category: Tools with integrated AI features, which refers to built-in enhancements within a particular piece of software. For example, after drafting a post in Google Docs, I can either copy and paste this text over to a standalone app like ChatGPT to improve my writing, or I can use the built-in or integrated Gemini for Workspace AI feature to make adjustments. Another example would be: I can either use Midjourney as a standalone tool to generate images for a presentation, or I can create an image directly within Google Slides by using, again, Gemini for Workspace.
In these instances, ChatGPT and Midjourney are Standalone AI tools, while Google Docs and Google Slides are tools with integrated AI features.
Third, a custom AI solution is an application that's tailor-made to solve a specific problem. For instance, Johns Hopkins University developed an AI system with the sole objective of detecting sepsis. This custom AI solution improved diagnostic accuracy from 2–5% to an average of 40%.
And if you're like me and have no technical background, you might think, "Oh, custom AI solutions are extremely technical, and I'll probably never have to use them in the workplace." In reality, the opposite is true because well-designed custom AI solutions should require little to no technical know-how.
For example, when I was in the sales team, I managed over 200 clients every quarter, and performing research on every single one was obviously time-consuming. Nowadays, there are custom AI solutions that can ingest all the information about those 200 clients—taking into account factors like seasonality, historical data, and industry trends—and rank those clients by how likely they are to need assistance, helping the salesperson prioritize their time.
By the way, if you're thinking of buying Google's AI Essentials course, don't! Because I only found out after I paid for it that you get the AI Essentials course for free if you enroll in the Google Project Management certification on Coursera, which is kindly sponsoring this portion of the video.
Regular viewers know that I have a full-time job, and project management obviously plays a large part in my day-to-day. To be transparent, though, I'm mainly self-taught since there wasn't a go-to course back in the day. But I recently started the Project Management certification on Coursera since it's now like the golden standard for project management.
Project management is literally applicable to all industries and roles. So if you want to be more organized in the workplace, click the link below to enroll in the Google Project Management certification and unlock the AI Essentials course for free. Thank you, Coursera, for sponsoring this part of the video!
Back to my takeaways.
The second learning from the course is a prompt engineering tip, and it's to surface the implied context.
To illustrate this, imagine your vegetarian friend asks you for restaurant recommendations. You instinctively reply with vegetarian-friendly options, even if your friend doesn't explicitly say, "Hey, idiot, make sure it's vegetarian!"
Here, the fact that your friend is a vegetarian is implied context and needs to be explicitly stated when communicating with AI tools like ChatGPT and Google Gemini.
Another example might be: You're preparing to negotiate a raise with your boss. You know in your head that last year you received a 10% increase. This year, you're the highest performer on the team, and the industry average is a 12% increase. So you decide to ask for a 15% raise.
If you leave out all that implied context when brainstorming negotiation techniques with an AI tool, you will receive a lower-quality (aka more generic) output.
Takeaway number three: Know when to use Zero-shot and Few-shot prompting.
In a nutshell, the word shot simply means examples.
Zero-shot means you use a prompt with no examples.
One-shot means you include one example.
Few-shot means you include two or more examples.
For instance, a zero-shot example might look something like: "Write me a pickup line for Bumble." (Which is a completely hypothetical scenario I would never condone, much less participate in.)
A one-shot prompt would be: "Write me a pickup line for Bumble. Reference this pickup line my friend used that worked well for him," and you include an example of what your friend wrote.
A few-shot prompt would look the same as one-shot, with just two or more examples of successful pickup lines.
And as you can imagine, the more relevant examples you provide the AI tool, the more relevant the output.
Takeaway number four: Use Chain of Thought prompting for complex tasks.
I've talked about this concept in previous videos, but I really like this simple and straightforward definition from Google's course:
"When you divide a single task into more manageable steps, you help the large language model produce accurate and consistent results."
Takeaway number five: Understand the limitations of AI.
Broadly speaking, there are three main limitations:
The underlying data that is used to train AI models may be biased.
There simply wasn’t enough information in the source training data on a given topic.
Hallucinations—AI outputs that are straight-up factually inaccurate.
When it comes to pros and cons, I'm going to start with who this course is NOT for.
This course is not for you if you're already using AI tools like ChatGPT and Google Gemini as part of your daily workflow and you're looking to dive deeper into specific AI use cases.
That being said, this is an excellent beginner-level course with three huge advantages:
You're learning from Google employees who are established experts in AI.
The visuals are amazing—they use simple graphics to explain complex topics.
The interactive elements are surprisingly helpful—the assignments are actually useful.
To sum up: This course is great for beginners, visual learners, and those looking for a legit certificate that can attract employers.
If you found this helpful, you might want to check out my summary of Google’s free AI course—that’s a bit more conceptual but equally important.
In the meantime, though, as usual—have a great one!