5 Skills to Work Better with AI

 


AI is everywhere, and it’s here to stay. From virtual assistants to powerful decision-making tools, AI is shaping the way we work and interact with technology. But simply using AI isn’t enough—you need to understand how to make the most of it.

Many people, even in everyday interactions with AI-powered tools like ChatGPT, Gemini, or DeepSeek, struggle to get the results they expect. This isn’t because AI is flawed, but because working effectively with it requires a certain set of skills. Knowing how to phrase your queries, provide the right context, and interpret AI’s responses can make all the difference.

Whether you use AI for work, creativity, or problem-solving, keeping these five key skills in mind will help you unlock its true potential.

1. Know Where to Use AI

AI isn’t a one-size-fits-all solution. While it excels at processing vast amounts of data and analyzing lengthy documents, it’s not always necessary—or even useful—in every situation. It’s not a magic wand, and understanding its limitations is key.

A good way to decide if AI is suitable for a task is to check whether the problem involves repetitive patterns, large-scale data processing, or text/image analysis. If the task requires deep human judgment, emotional intelligence, or creative problem-solving, AI may only serve as an assistive tool rather than a replacement. Knowing when and where to apply AI ensures you use it effectively.

2. Master the Art of Clear and Structured Instructions

Although AI may seem like it understands you, it doesn’t—it makes predictions based on patterns in the data it has been trained on. It doesn’t think or reason like humans; instead, it processes your instructions by breaking them down into numerical representations (vectors) and matching them with similar patterns from its training data.

Since AI lacks true comprehension, the way you phrase your input significantly impacts the output. This is where prompt engineering comes in—the skill of crafting precise instructions to guide AI towards the desired response. AI interprets input text by breaking it into tokens (smaller text units), analyzing context, and predicting the next most likely outcome. The more structured and detailed your prompt, the better AI can align its response with your expectations. Clear, well-structured instructions with proper context lead to better results, while vague or ambiguous inputs can produce unexpected outcomes. Choose your words carefully!

3. Use Examples to Guide AI Towards Better Responses

AI learns from data much like a child learns from experience. Pre-trained models like ChatGPT recognize patterns, but without clear examples, they may generate generic responses.

Providing examples in your prompt helps AI map your intent more accurately in its internal representation, often referred to as vector space. Your examples act as guideposts, helping AI recognize patterns and align responses more accurately. Since AI doesn’t truly understand meaning but works with patterns and associations, your examples act as reference points, helping it align responses more closely to your expectations. If you need a summary in a specific way, including a sample helps AI generate a response in the right tone and structure. Well-structured examples act as guardrails, making AI outputs more accurate and relevant.

4. Define Your Expectations Clearly for Better Results

When delegating tasks to humans, you explain the objective and may even break it down into steps. If you’ve assigned it to the right person, you can trust their judgment and experience to fill in the gaps and deliver the expected outcome.

With traditional programming, however, you must define every step explicitly. Computers follow exact instructions (code), and the responsibility lies entirely with the programmer to ensure the logic leads to the desired outcome. There is no room for assumption—the program only does what it’s told.

AI sits somewhere in between. Unlike traditional programming, you don’t have to write step-by-step instructions, but you still need to define your expectations clearly. The AI model relies on patterns and predictions rather than strict logic. Providing a well-structured prompt with a clear objective, expected outcome, and guiding steps can significantly improve results. A concept called chain of thought helps AI reason by breaking down complex tasks into intermediate steps, leading to more accurate and thoughtful responses. For instance, asking AI to 'explain step by step' encourages it to reason through the process rather than jumping to a conclusion. 

5. Refine and Experiment to Improve AI Interactions

AI isn’t plug-and-play—it takes time, data, and iteration to deliver reliable results. Even with pre-trained models like ChatGPT, Gemini, or DeepSeek, getting the desired output requires careful prompt crafting and refinement. These models don’t inherently “understand” tasks; they generate responses based on patterns, so small tweaks in wording, context, or examples can significantly improve results.

For specialized AI applications, the learning curve is even steeper. Traditional AI models need rigorous training, testing, and fine-tuning before they are production-ready. Whether using pre-trained or custom AI, patience and experimentation are key to achieving consistent, high-quality outcomes.

 


AI is a powerful tool, but its effectiveness depends on how well you interact with it. By developing these skills, you can turn AI into a valuable ally rather than a frustrating black box.


- By Rajashree Rajadhyax, CoFounder, Cere Labs

 

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