A Hands-On Review of Google’s AI Essentials Course: 5 Key Lessons, Honest Pros & Cons, and Is the Certificate Worth It?

A Hands-On Review of Google’s AI Essentials Course: 5 Key Lessons, Honest Pros & Cons, and Is the Certificate Worth It?

Written by Massa Medi

Last week, I ventured deep into the world of artificial intelligence by dedicating five hours and spending $49 on Google’s new AI Essentials course for beginners. My objective? To extract the most worthwhile insights, evaluate the course’s strengths and weaknesses, and, of course, answer the burning question: will the certificate increase your chances of… getting paid? (Not “laid”—that was a mischievous slip. But hey, if knowledge and new skills improve your professional and personal prospects, more power to you.)

Here, I’ll share the five biggest takeaways from the course, break down who the course is really for, and give you the candid truth about whether Google’s shiny certification is actually worth your investment.

Takeaway #1: The Three Types of AI Tools—And When You’ll Use Them

Let’s start by establishing some foundational knowledge. According to Google’s course, there are three broad categories of AI tools that you’ll see popping up everywhere:

  1. Standalone AI Tools: These are AI-powered apps or software that operate independently and require minimal setup. Think familiar chatbots like ChatGPT, Gemini, Claude, and Perplexity. You’ll also find specialized apps such as Speeco (for voice transcription), Otter (for meetings), Midjourney (for image generation), and Gamma (for slide decks and presentations). Despite their different purposes, they’re called “standalone” because you can use them directly via their websites or apps—no need to hook them into other software or services.
  2. Tools with Integrated AI Features: These are regular programs you probably already use, like Google Docs or Google Slides, but supercharged with AI enhancements. For instance, after drafting a document in Google Docs, you could either copy-paste the text into a standalone AI tool like ChatGPT for suggestions, or you could use Google’s integrated Gemini AI right within Docs to improve your writing. Similarly, you might use Midjourney (standalone) for images, or generate them directly in Google Slides using its built-in AI. The key difference here: Standalone tools are their own destination, whereas integrated AI features enhance software you already use.
  3. Custom AI Solutions: These are tailor-made AI applications designed to address specific business or organizational problems. For example, Johns Hopkins University developed an AI system specifically to detect sepsis in patients. The results were dramatic—increasing diagnostic accuracy from 2–5% all the way up to an average of 40%. And here’s a big misconception: you don’t need a computer science degree to benefit from custom AI. Well-designed solutions require little to no technical background for users. As someone who used to handle over 200 sales clients each quarter, I know firsthand how time-consuming research can be. Now, custom AI can ingest vast customer data, weigh factors like seasonality and industry trends, and recommend where a salesperson should focus their energy next. It’s all about empowering non-technical users with smart solutions.

Pro tip: If you’re thinking of buying Google’s AI Essentials course, hold up! I discovered after I paid that you can actually get the AI Essentials course for free—with enrollment in Google’s Project Management Certification on Coursera. (Yes, Coursera is kindly sponsoring this portion of my recap.) As someone who works full time and dabbles in project management daily, I can confirm: this certification is now considered the gold standard in the field. If you’re aiming to be more organized and tech-savvy at work, definitely consider checking out the Google Project Management Certification through the link below. You’ll unlock the AI course for free, your wallet will thank you, and your resume will thank you even more.

Takeaway #2: Master Prompt Engineering by Surfacing Implied Context

On to a prompt engineering essential—surfacing the implied context. Let’s play out a scenario: your vegetarian friend asks for restaurant suggestions. Naturally, you’d recommend vegetarian-friendly places, even if they don’t spell it out for you. Their diet is the implied context guiding your response.

But when you communicate with AI tools—like ChatGPT or Google Gemini—the AI doesn’t know your context unless you say it explicitly. If you’re prepping to negotiate a raise and last year you had a 10% bump, this year you’re a top performer, and the industry average is a 12% raise, you’d probably aim for 15%. If you ask an AI for negotiation advice without including this crucial backstory, you’ll get generic, less relevant results.

So: always spell out your context and specifics when writing prompts. If you’re curious about prompt engineering, I’ve got a whole video on crafting the perfect prompt, plus a free workspace toolkit with my top five productivity prompts—links are down below.

Takeaway #3: When to Use Zero Shot, One Shot, and Few Shot Prompting

Here’s a term you’ll see thrown around in the AI world: shot. It just means “example.” Here’s the quick breakdown:

The takeaway: The more relevant examples you give, the more tailored and accurate the AI’s response will be. (For the record, if my future spouse is reading this: I’ve never used dating apps. This is just for educational purposes.)

Takeaway #4: Use Chain of Thought Prompting for Complex Tasks

One of my favorite insights from Google’s course is the concept of “chain of thought” prompting. It means breaking a big task down into smaller, manageable steps so the AI can guide you through each phase with precision.

For example, if you need a cover letter:

This not only results in a finer-tuned, more personal cover letter, but it also gives you full control at each step. Curious if it really works? I’ve documented this technique in a separate video, specifically for job seekers—find the link below.

Takeaway #5: Always Understand the Limitations of AI

It’s tempting to treat AI as a miracle worker, but you need to be aware of three major limitations:

  1. Data Bias: An AI is only as good as the data used to train it. If a text-to-image AI only sees minimalistic designs, it won’t understand how to make something bold and flashy.
  2. Knowledge Gaps: AI models have data cut-off dates and aren’t always up-to-date with the latest events. If you ask it about something that happened after its last training, results may be spotty or nonexistent.
  3. Hallucinations: Sometimes, AI just makes things up (AKA “hallucinates”), offering answers that are factually incorrect. Sometimes that’s fine when you’re brainstorming. But it’s risky for high-stakes tasks—like figuring out which supplements to take for your health. Always double-check when your decisions matter.

Course Pros & Cons: Who Should (and Shouldn’t) Take Google’s AI Essentials?

Let’s get honest: This course isn’t for you if you already use AI tools like ChatGPT or Google Gemini daily and want deeper, advanced knowledge. The course has a solid overview of important concepts, but its real-world examples can be a little… vague. For instance, they mention a company using AI to reduce customer service wait times—and leave it at that. It would have been more helpful to clarify how the company used AI, whether it was standalone or custom, how the staff was trained, and how they ensured the AI didn’t hallucinate (especially when dealing with real customers).

That said, this course is fantastic for beginners or visual learners, and here are three big reasons why:

  1. Expertise: You’re learning directly from Google employees—people who live and breathe AI (and who probably don’t moonlight as YouTube comedians).
  2. Visual Explanations: The graphics and analogies are top-notch. For instance, they use the analogy of a car (AI tool) and its engine (AI model)—the model powers the tool, and the tool helps you complete your task. It’s simple, smart, and helps demystify complex concepts.
  3. Interactivity: The activities and graded assignments actually teach you core concepts. The quizzes are challenging enough that you can’t just click through—you’ll need real understanding to score the 80% pass mark.

Other benefits include a curated list of beginner-friendly AI tools and a handy glossary of today’s must-know AI vocabulary.

The bottom line: This course is a terrific jumping-off point for beginners, especially visual learners, job seekers, and the AI-curious. Plus, with a legitimate certificate from Google at the end, you’ll have something impressive to flash to potential employers—or, who knows, even prospective partners.

Found this guide helpful? You might enjoy my summary of Google’s free, more conceptual AI course (link below). Until next time—stay curious, stay learning, and, as always, have a great one!