To use AI in daily life, five learnings make a difference: knowing the three categories of tools (standalone like ChatGPT, with integrated AI like Google Docs, and customized solutions), writing prompts with clear context, using examples to improve the response, applying chain-of-thought reasoning in complex tasks, and understanding the limits of the technology.
The Artificial Intelligence Revolution: Why You Need to Master AI Now?
Over the past few months, I've been diving deep to understand what really makes a difference when it comes to Artificial Intelligence. And believe me, it's no exaggeration to say: AI is no longer the future, it's already shaping our present. Those who don't start moving now will be left behind.
Thinking about this, I've organized 5 essential learnings about Artificial Intelligence that anyone starting out needs to understand so they don't get lost.
Learning 1: The 3 Essential Categories of AI Tools
To get started, we can divide AI tools into three major categories. Of course, this market evolves quickly, but this is the simplest and most practical way to understand the landscape today.
- Standalone Tools: These are AI tools that work independently. You access a website or application and start using it, without needing integration with other programs. Some famous examples are ChatGPT, Gemini and Claude. In the design area, we have Leonardo.IA for image generation, for example.
- Tools with Integrated AI: Here, artificial intelligence is already a built-in feature in programs you already use in your daily life. Google Docs is a great example. Instead of copying and pasting text into ChatGPT to improve it, you can use Gemini for Workspace, already integrated.
- Custom AI Solutions: These are customized AI solutions, made to order to solve a very specific problem within a company. A classic example is Johns Hopkins University, which created an AI to diagnose sepsis, raising diagnostic accuracy from 2-5% to 40%. This shows the power of Artificial Intelligence when applied to a specific problem.
You might think that custom solutions are only for large corporations, but the truth is that many companies already use them to optimize internal processes in an automated way, such as customer profile analysis, without the end user needing technical knowledge.
Learning 2: The Key to AI: Clarity in Your Request (Prompt)
If you want Artificial Intelligence to really help you, you need to learn to be clearer in your requests. Tools like ChatGPT or Gemini don't guess what you think, so context is everything.
If you're negotiating a raise, for example, it's not enough to ask "Write a raise request". The secret is to include the complete context:
- Your adjustment from last year
- Your team's performance this year
- The market average salary
The more context, the more accurate the AI's response will be.
Learning 3: Use Examples to Improve the Response (Shot)
For Artificial Intelligence to deliver more accurate responses, you need to understand the concept of "Shot", which is the number of examples you provide in your prompt:
- Zero Shot: You make the request without giving any example
- One Shot: You give one example
- Few Shot: You give two or more examples
The more examples you provide, the more the AI will understand the tone, style and intention of your message, generating results much more aligned with what you need.
Learning 4: Use Chain of Thought for Complex Tasks
"Chain of Thought" is a simple technique: breaking a large task into smaller steps. This avoids generic responses and leads to a much more accurate result.
If you need to create a presentation, for example, it's not enough to ask the AI to "Create a presentation about market trends". The correct way is:
- Step 1: "List 3 market trends for 2025, focused on my sector."
- Step 2: "Based on these trends, send me an eye-catching title."
- Step 3: "Now set up the presentation skeleton with the topics."
This simple but powerful method will make the AI deliver a result much closer to what you need.
Learning 5: Understand AI Limitations
Despite being a powerful technology, Artificial Intelligence has its limitations. Understanding these limits is the key to using it intelligently:
- Biased data: AI learns from the data it receives. If that data is limited or has biases, responses can be distorted.
- Lack of updated information: Many models don't have access to real-time information. Always check the model's data cutoff date for questions about recent events.
- Hallucinations: Yes, AI can invent information. This is useful for brainstorming, but on important matters (such as critical company data), you should always review the response before using it.
To deepen your understanding of AI's impact on companies, a recent PwC study points out that sectors more exposed to AI recorded an almost fivefold increase in productivity. You can read the full report here.
Conclusion
Mastering Artificial Intelligence is not about knowing how to program, but rather understanding how the tool works and knowing how to use it strategically.
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