99% of Beginners Don't Know the Basics of AI

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 video to share 5 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 1, broadly speaking, there are 3 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 Chachabitty, 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 Chachabitty 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, Chachabitty and Midjourney are standalone AI tools, and 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, John 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, take 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, you get the AI Essentials course for free if you enroll in the Google Project Management Certification on Coursera, who 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 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. If you want to dive deeper into this, I have an entire video on how to write the perfect prompt so I'll leave a link to that down below. And if you want to make a copy of my five favorite prompts for productivity, I'll leave a link to my completely free workspace toolkit as well. 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. For the record, if my future wife is watching this, I've obviously never done this myself. This is just an example for this video. As a matter of fact, I don't even use dating apps. 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. A relatable real world example would be writing a cover letter. Option one, you share a current resume and the job description with the chat bot and just prompt it to write you a cover letter. Option two, with chain of thought prompting, you break the large task, writing an entire cover letter into manageable steps with step one being based on my resume and the job description, write an attention grabbing hook for my cover letter. Then after making some minor tweaks to the hook paragraph, step two would be pasting that back into the chat bot and asking it for the body paragraph, rinse and repeat for the closing paragraph. If you want proof that this works, I have an entire video where I show job seekers how to use chain of thought prompting to not only write cover letters, but to also improve their resumes. I'll leave a link down below. Takeaway number five is a topic that many of us, including myself, tend to overlook when using AI tools, and it's to understand the limitations of AI. Broadly speaking, there are three main limitations. First, the underlying data that is used to train AI models may be biased. If a text to image model is only trained on minimalistic graphics, it might not be able to produce flashy and bold designs. Second, there simply wasn't enough information in the source training data on a given topic. Many AI models have a cutoff date, so if you ask for something that happened recently, it won't have enough data on that topic to give you an accurate answer. Finally, hallucinations are AI outputs that are straight up factually inaccurate. Sometimes this is a feature and not a bug, when you're brainstorming new ideas for example. Other times, hallucinations perpetuate false information, so you definitely want to double check your answer for what I call high stake tasks. For example, what sort of supplement or vitamins to take given your health goals. 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. And that's because although they do a great job explaining complex topics, the examples they give in the course are pretty vague. For instance, in one of the lessons, they gave an example where a company uses AI to decrease customer service response times. And that was it. That was the entire example. It would have been nice to dive a bit deeper and talk about whether this company used a standalone AI tool or a custom AI solution, how they trained the workers to use the AI system, how they grounded the data so that the AI system didn't hallucinate since this was a customer service application, right? Stuff like that. That being said, this is an excellent beginner level course with three huge advantages. First, you're learning from Google employees who are established experts in the field of AI. They know what they're talking about, and they're not just some random person on the internet who enjoys making crass jokes. Second, as a visual learner, I'm amazed at how they're able to use simple graphics to explain complex topics. For example, they analogize AI tools and AI models to a car and its engine. The model slash engine provides the underlying capabilities while the tool, like the car, helps you in completing the task. Third, the interactive elements are surprisingly helpful. The activities or homework are well-designed in that they actually help you learn a key concept from that lesson. The graded assignments are also not that easy. They're mainly multiple choice, but you actually have to pay attention to get 80% and pass the quiz. Last but not least, the course provides a curated list of AI tools for beginners to explore and includes a glossary of common AI terms that are now prevalent in our daily lives. To sum up, this course is great for beginners, visual learners, and you can use the legit certificate you get at the end to attract prospective employers and or partners. If you found this video 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.