The Secret to AI Proficiency: Why Repetition Is the Key to Mastery

By Richard Sebaggala

Jensen Huang, CEO of NVIDIA, once said: "You are not going to lose your job to AI, but rather to someone who uses it." This statement gets to the heart of what separates those who succeed in the age of AI from those who fall behind: a willingness to learn, adapt, and practice.

Since I started delivering training on the use of generative AI in research, I’ve met countless people who want to realize the full potential of AI. Many are looking for shortcuts — tools that promise immediate productivity gains with minimal effort. What is often overlooked, however, is the dedication and time required to achieve true proficiency. My journey with generative AI has been anything but instant. It has been a process of structured learning and deliberate repetition, sometimes requiring hours of practice to master just one tool.

 

The remarkable results I have achieved are not due to my talent alone. They came about because I understood and embraced the value of repetition — what Stan Goldberg aptly calls "neurological secret sauce" in his studies on memory and learning. Repetition may sound mundane, but it's the cornerstone of mastery in any field, whether it’s delivering a flawless speech, performing a symphony or, as in my case, refining workflows with generative AI.

Goldberg explains that the difference between beginners and experts lies in the strength of their "memory layers". When you start a new task, your first actions will feel clumsy and awkward. Think back to the first time you typed on a keyboard or learned to drive a car. But over time, your neural connections will strengthen, and the process will become second nature.

 

My first interactions with AI tools like ChatGPT were far from seamless. I often questioned my prompts, overlooked important features and misinterpreted the results. But through repetition, I was able to create a mental "blueprint" for using these tools. Today, writing structured prompts, refining results based on feedback, and integrating AI-generated content into research workflows feels as natural as typing.

Each iteration has strengthened my skills. Like a carpenter who masters his tools after thousands of uses, I have honed my expertise step by step. However, repetition alone is not enough. As Goldberg points out, training must be precise. Michael Phelps’ coach famously advised him not to practice strokes that deviated from perfection. And why? Practicing mistakes only reinforces bad habits.

 

This principle also applies to AI. In my practice sessions, I was not content to randomly perform queries. I critically examined the results, identified gaps and refined my techniques. For example, when l was practicing  literature review tools such as Avidnote or Elicit for systematic review , l compared the AI-generated summaries with those written by humans to identify weaknesses and adjust my prompts. This focus on "perfect practice" helped me internalize the subtleties of using AI effectively, which led to consistently better results.

 

Malcolm Gladwell popularized the idea of the "10,000 hour rule" in his book, in which he emphasizes that mastery requires an extraordinary amount of deliberate practice. Even though AI tools are designed to simplify tasks, maximizing their potential requires a similar commitment to practice. True mastery — the kind that allows you not only to use AI, but to realize its full potential — is based on time, effort and intention.

 

In my experience, the key to unlocking the transformative potential of AI is to regularly invest hours in learning and refining AI workflows. Every hour you spend practicing structured prompts, analyzing feedback, and applying the tools to real-world problems adds to the cumulative expertise that separates a proficient user from a casual user. Just as musicians, athletes and writers achieve greatness through thousands of hours of practice, AI users must approach their craft with the same dedication.

 

Even mastery requires nurturing. Without reinforcement, skills diminish. Jascha Heifetz, the legendary violinist, practiced scales daily despite his world-renowned talent. Similarly, I regularly engage with AI tools to ensure my knowledge remains up to date.

 

When tools like Chatgpt, Gemini and Avidnote release updates, I make it a priority to practice with their new features. I subscribe to several AI platforms to keep up to date with new developments in the AI industry. This continuous engagement ensures that I stay up to date and adapt to advances rather than becoming complacent.

One of the most valuable lessons from Goldberg is to "practice slowly". In the early days of learning AI tools, I deliberately slowed down the pace to focus on understanding each step. By resisting the urge to rush, I built a solid foundation that eventually allowed me to work faster and more efficiently.

For example, before I ventured into complex tasks such as automating literature reviews, I started with the simple creation of notes. This deliberate progression gave me the confidence and competence to tackle larger, more complicated projects with fewer mistakes.

A common mistake many learners make is practicing in an isolated environment. Goldberg’s analogy of delivering a speech in a quiet room — only to run into trouble in a noisy room — is very apt. Mastery requires the application of skills in dynamic, real-world environments. In the early days of learning AI, I initially practiced in a controlled environment and experimented with tools in private. But to really hone my skills, I decided to apply them in live workshops, group discussions and collaborative research projects. These real-world applications tested my adaptability and boosted my confidence so that my skills were not just theoretical, but practical.

Repetition is not the enemy of creativity, it is its foundation. Without structured practice, even the most innovative ideas fail to be realized. Just as athletes and artists rely on repetition to excel, mastering generative AI requires deliberate, repetitive practice. For those looking for shortcuts, this is a reminder: mastery isn’t about finding the perfect tool, it’s about cultivating the right habits. Take the time to practice, refine your techniques and make repetition your own. The results will speak for themselves.

As Stan Goldberg eloquently puts it, repetition reinforces the "blueprints" that guide our actions. Mastery is not a destination, but an continuous journey shaped by the time and effort we invest every day.

In the words of Jensen Huang, your job is not in danger because of AI, but if you do not learn how to use it, it's in danger. Embrace the process and let repetition and a commitment to mastering the 10,000-hour rule be your best allies in mastering the tools that will shape the future.