Google AI Essential Course Review : My Takeways
Google AI Essential Course Review- Last week I spent 5 hours to complete Google’s AI Essentials course for beginners. I am learning artificial intelligence everyday. And I love to explore new courses and ai tools.
In this article, I am sharing five key takeaways from the course, the pros and cons. And to give you a definitive answer as to whether the certificate you receive at the end will indeed get you paid. Paid more because you now have a new knowledge.
You can also take Google AI Essential course which is available on Coursera. You can join and take the course for free but you need to pay for the certificate. If you just want to learn about AI then you can do it for free.
Google AI Essentials Review – My Takeaways
I have learned this course and found it beneficial especially if you are a beginner. I have spent my time learning Google AI essential course. This course helped me in understanding the AI tools and prompt engineering. I have got these learning. Here are my 5 takeaways from the course-
Takeaway 1: The Three Types of AI Tools
Broadly speaking, there are three types of AI tools out there.
1. Standalone Tools
Standalone tools are AI powered software designed to work independently with minimal setup.
This includes:
- General purpose chatbots like ChatGPT, Gemini, Claude, Perplexity
- Specialized apps like Spico, Otter AI, Midjourney, Gamma
They are classified as standalone because they’re accessed directly through websites or apps without requiring integration.
2. Tools With Integrated AI Features
These are built-in AI enhancements inside existing software.
Examples:
- Draft a post in Google Docs → use built-in Gemini for Workspace
- Create an image in Google Slides → use Gemini, instead of going to Midjourney
Here, ChatGPT and Midjourney = standalone tools.
Google Docs and Slides = tools with integrated AI features.
3. Custom AI Solutions
Custom AI solutions are tailor-made to solve a specific problem.
Example:
Johns Hopkins University built an AI system to detect sepsis, improving diagnostic accuracy from 2–5% to ~40%.
And even if you have no technical background, custom AI solutions are often designed so end-users don’t need technical knowledge.
Real example:
As a salesperson managing 200 clients, modern custom AI solutions can ingest client data, analyze seasonality and trends, and rank clients by likelihood of needing assistance.
Takeaway 2: Surface the Implied Context
A prompt engineering tip: state the implied context explicitly.
Examples:
- Your vegetarian friend asks for restaurant recommendations → you automatically filter vegetarian places.
- You negotiate a raise → your historical raise %, performance level, and industry averages are implied context.
But if you don’t explicitly state this implied information in your prompt, AI tools produce low-quality, generic answers.
If you want to go deeper, I have a video on how to write the perfect prompt.
You can also copy my five favorite productivity prompts from my free workspace toolkit.
Takeaway 3: Know When to Use Zero and Few Prompting
The word means examples:
- Zero→ no examples
- One→ one example
- Few→ two or more examples
Examples:
- Zero: “Write me a pickup line for Bumble.”
- One: “Write me a pickup line for Bumble. Reference this example that worked for my friend.”
- Few: Same as above but with two or more examples.
More relevant examples = more relevant output.
Takeaway 4: Use Chain-of-Thought Prompting for Complex Tasks
Google defines it perfectly:
When you divide a single task into manageable steps, you help the model produce accurate and consistent results.
Example: Writing a cover letter
Option 1 (Not great)
Upload your resume + job description → “Write me a cover letter.”
Option 2: Chain-of-Thought (Much better)
- Step 1: “Write an attention-grabbing hook for my cover letter.”
- Tweak that.
- Step 2: “Using this hook, write the body paragraph.”
- Step 3: Then the closing paragraph.
Do you want to learn how to use chain-of-thought prompting to write better resumés and cover letters.
Takeaway 5: Understand the Limitations of AI
AI has a lot of uses and it eases the workload. There are three main limitations:
1. Biased Training Data
If a text-to-image model only trains on minimal visuals, it can’t produce bold, flashy designs.
2. Insufficient Data
Many models have a cutoff date.
If you ask about something recent, the model simply doesn’t have enough information.
3. Hallucinations
AI can generate factually incorrect information.
Sometimes this helps when brainstorming creative ideas.
Sometimes it’s dangerous-like when asking what supplement to take.
Pros and Cons of Google’s AI Essentials Course
Who This Course Is NOT For
This course isn’t for you if:
- You already use AI tools daily
- You want deep, practical, specialized AI use cases
The examples in the course are often vague.
For instance, they mention a company using AI to reduce customer response time but give no detail on:
- What AI tool they used
- Whether it was standalone or custom
- How the team was trained
- How data grounding was handled
Why This Course is Great for Beginners
1. Taught by Google Employees
You’re learning from experts, not random people online making jokes (like me).
2. Extremely Visual
They explain complex AI topics using simple graphics.
Example:
AI tools = cars
AI models = engines
3. Useful Interactive Elements
Assignments require real attention.
You need 80% to pass the quizzes.
4. Curated Tools & Glossary
The course includes:
- A curated list of beginner-friendly AI tools
- A glossary of common AI terms
Join Google AI Essential
Conclusion
I hope you like this Google AI Essential Course Review. This course is great for beginners, visual learners, and anyone who wants a legit certificate to show employers or partners. This course will definitely increase your knowledge about artificial intelligence. You can learn more about AI as there are several free and paid AI courses available. If you have any query then comment. Smile