Last week, I spent five hours and $49 taking Google’s new AI Essentials course for beginners. And because I’d love to make those dollars back (so I can keep up my very real habit of talking to AI instead of real people), I made this video to share my five biggest takeaways—with straight talk on the pros and cons and the big question: Is this certificate actually worth your time and money? And will it help you get paid, not… well, you get the idea. That came out weird.
Takeaway #1: The Three Types of AI Tools
Alright—first, the course breaks down all AI tools into three types.
One: Standalone tools.
These are the all-in-one, ready-to-go apps you can just open and use. We’re talking chatbots like ChatGPT, Gemini, Claude, and Perplexity, plus specialty apps like Otter.ai, Midjourney, and Gamma. Different tools, different purposes, but all standalones you can launch straight from their websites or apps—no fuss, no extra software needed.
Two: Tools with Integrated AI Features.
This means programs like Google Docs or Slides, where AI isn’t the star attraction—it’s just built in. So, for example, you could copy your draft from Docs and paste it into ChatGPT (standalone), or just use the built-in Gemini Workspace tool to spice things up right there. Same thing with images: you could use Midjourney solo, or generate one inside Google Slides using Gemini. Standalone tools are like Swiss Army knives. Integrated AI shows up quietly where you’re already working.
Three: Custom AI Solutions.
These are tailor-made applications built for specific needs. Like, Johns Hopkins made an AI just to detect sepsis—and it boosted accuracy from 2-5% to around 40%. If you’re not a techie, you might be thinking, “Custom AI? That sounds way over my head.” Honestly, not really—the point is for these tools to be simple to use. For example, when I was on a sales team juggling 200 clients a quarter, researching all of them took ages. Now, there are custom AI solutions that can look at all your client data, spot trends, even rank which clients might need help soon—super useful, no coding needed.
Quick tip: don’t pay for the AI Essentials course by itself, because I learned (too late) that you get it for free if you sign up for Google’s Project Management certification on Coursera—which also happens to sponsor this video. I’ve got a full-time job and manage projects every day, mostly self-taught, but decided to check out the certification since it’s become the gold standard lately. Project management fits basically any job, so if you want to get organized and unlock the AI Essentials course for free, check it out—link below. Thanks, Coursera.
Takeaway #2: Surface the Implied Context
Back to the course. Big prompt engineering lesson: always make the implied context explicit.
Think about it this way—if your vegetarian friend asks for restaurant picks, you just know to suggest veggie spots, even if she doesn’t say, “Hey, make sure there’s no meat.” That’s implied context. AI tools, though, need you to spell it out. So, if you’re brainstorming negotiation strategies with Gemini or ChatGPT, don’t just say, “I want a raise.” Tell the AI that last year you got a 10% bump, this year you’re the top performer, and the industry average is 12%—you’re aiming for 15%. All those background details help the AI give you a more tailored, useful response.
I cover this stuff in detail in a video about writing better prompts (link below), plus I share my favorite productivity prompts in a free toolkit if you want a cheat sheet.
Takeaway #3: Zero Shot vs. Few Shot Prompting
Here’s something basic but important: the word “shot” in “prompting” just means “example.” Zero shot is no examples; one shot means you provide one; few shot means two or more.
A zero-shot prompt might be, “Write me a pickup line for Bumble.” (Not that I’ve ever needed one—just an example!) A one-shot prompt: “Write me a pickup line for Bumble like the one my friend used that worked well”—then you give the example. Few-shot? Same idea, but give two or more examples. Basically, the more relevant examples you give, the better the AI gets at matching your tone or style. And, for the record, if my future spouse is watching: I don’t even use dating apps. This is just research.
Takeaway #4: Chain of Thought Prompting for Complex Tasks
Number four: when you’re dealing with trickier jobs, break them down with Chain of Thought prompting. The course puts it simply—split a big task into smaller steps and you help the AI stay accurate and consistent.
For instance, say you need to write a cover letter. You could just drop your resume and the job ad into the chatbot and ask for a cover letter. Or, with Chain of Thought, tackle it in pieces: “Based on my resume and the job description, write me a catchy opening.” Once that’s good, add, “Now write a body paragraph,” and so on. This way, you get a much more focused and polished result. I’ve got a whole video showing how to do this for cover letters and resumes—link down below.
Takeaway #5: Know the Limits of AI
Last key lesson: never forget there are real limits with these tools.
One—training data can be biased.
If you feed an image AI only minimalist graphics, don’t expect it to spit out flashy neon art.
Two—sometimes the AI just doesn’t know enough.
Most models are trained on data with a cutoff date. So, if you’re asking about something super recent, it might miss the latest facts.
Three—hallucinations.
That’s when AI just makes stuff up. Sometimes it’s entertaining (especially when brainstorming), but other times it can spread wrong or even dangerous info. If you’re asking about important things—like health decisions—always, always double-check before you act.
Who Should Take This Course (And Who Shouldn’t)
Let’s talk pros and cons. First, who’s this not for? If you already use ChatGPT or Gemini every day, or you want to go deep into advanced AI use cases, skip it. The course sticks to basics, and a lot of the examples are, honestly, pretty bland. Like, they’ll say, “A company used AI to cut customer service wait times,” and leave it at that—no details, no digging into how it worked or how they handled issues. Feels like a missed opportunity.
But for beginners, this course is excellent for three big reasons:
First, you’re learning from Google employees who know AI inside and out—not random YouTubers like me.
Second, visuals.
As someone who learns by seeing, I appreciated how the course used simple graphics—like comparing AI models and tools to a car and its engine. Makes everything click.
Third, it’s interactive.
The activities and quizzes actually make you apply what you learn. The quizzes aren’t a cakewalk, either—multiple choice, but you really have to pay attention to pass.
Plus, you get a handy list of beginner AI tools to try and a glossary of common terms that pop up in the AI world all the time now.
Final Verdict
So, bottom line? If you’re new to AI or a visual learner, you’ll get a lot out of this class. The certificate you earn is legit—good for resumes or even sparking conversations with future employers (or partners, not gonna lie). If you found this helpful, check out my review of Google’s free, more conceptual AI course—it’s got even more to chew on. That’s it for now—thanks for watching, and have a great one!
