Our Story
A father-and-son experiment in tennis, technology, and learning in the Generative AI era
It started with time on the court, questions at home, and the belief that learning is better when we do it together.
The beginning
Rally School did not begin as a software project. It began with tennis and a simple question:
“How can we make learning tennis easier for junior players and their parents?”
As Ethan developed as a junior tennis player, David and Ethan spent considerable time learning about tennis together: technique, practice, equipment, junior development, competition, the mental game, movement, and the many questions junior players and their parents encounter.
Ethan is a dedicated junior tennis player who also enjoys helping younger children learn the game. He has helped introduce younger children—including first-time players around ages 6–7—to simple activities and drills that help them learn the sport and build confidence.
That experience inspired another question:
“What if we could organize some of what we are learning into a simple online resource that could help other junior players and families?”
That idea became Rally School — Junior Tennis Fundamentals.
From court to website
Each stage raised a better question—and gave us something new to learn.
David learns alongside Ethan as Ethan develops as a junior player.
Can we organize what we are learning into something useful for other junior players and parents?
Use conversations with ChatGPT to define users, problems, learning pathways, and website structure.
Decide what a beginner, developing junior player, tournament player, and parent should be able to find.
Translate the product requirements into HTML, CSS, JavaScript, and a working website.
Look at the site as a parent, junior player, and beginner. Is it clear? Is it useful? Is something confusing?
Refine navigation, beginner guidance, player levels, technique content, and practice materials.
Create Ethan's Easy First Tennis Lesson, red-ball drills, practice plans, Ethan's Tips, and carefully selected video examples.
Deploy Rally School online and continue improving it as we learn more about tennis and technology.
Start with why
One of our biggest lessons was that an AI-assisted project does not need to begin with programming. We started with questions.
The requirements came before the code.
Working together
“AI helped us build the website. It didn't decide why the website should exist.”
We still needed to define the problem.
Understand the users.
Decide what was important.
Provide clear requirements.
Review AI-generated work.
Test whether the site worked.
Identify errors or weak ideas.
Decide what to improve next.
Step by step
Rally School grew in phases. Each version gave us a foundation for the next question.
We developed Rally School branding, Junior Tennis Fundamentals, “New to Tennis? Start Here,” “Explore Player Levels,” About Rally School, and the creator credit.
We asked what someone playing tennis for the first time actually needed.
“Reading about tennis isn't enough. You have to play.”
This led to Beginner Tennis Training, Red Ball Drills & Practice Plans, Ethan's Easy First Tennis Lesson, a 45-minute lesson with seven stages, Ethan's Tips, coaching adaptations, and YouTube demonstrations.
A new entry path
The starting point can now move closer to the idea—but technical understanding still matters.
They help people communicate requirements, recognize errors, understand limitations, evaluate AI output, and build safer, better solutions. Generative AI can dramatically lower the barrier between an idea and a working prototype; it does not remove the need to understand and evaluate what gets built.
The bigger lesson
Traditionally, someone interested in technology might ask, “How do I code this?” That question remains useful. But more questions are becoming increasingly important.
These questions build curiosity, communication, critical thinking, judgment, creativity, experimentation, domain knowledge, and persistence.
Ethan's role
Ethan was not simply the subject of the site. His experiences as a developing junior player helped shape the resource.
His experience helping younger beginners also contributed to the Beginner Tennis Training section. Working on Rally School became a learning experience involving tennis, teaching, communication, product thinking, technology, and Generative AI.
Practice and iteration
Evidence over assumptions
Using AI did not mean every answer or code change was correct. We still had to investigate what was really happening.
Local and hosted sites showed different versions.
Two similar GitHub repositories had to be distinguished.
We determined which repository powered rallyschoolonline.com.
Browser caching sometimes required a hard refresh.
GitHub Pages had to deploy the correct version.
YouTube IDs needed verification—not blind trust.
Embeds behaved differently locally and in production.
Checklist storage keys and JavaScript hooks needed protection.
AI suggestions still had to improve the real user experience.
Learning to debug AI-generated work was itself an important skill.
The resource today
A growing collection of practical paths for junior players and their families.
Orientation for players and families new to tennis.
Development guidance for players progressing in junior tennis.
Fundamental tennis skills and educational content.
Movement and court-positioning concepts.
Resources for the mental side of tennis.
Development checklists players can use to reflect on progress.
Red-ball drills and Ethan's Easy First Tennis Lesson.
Selected YouTube demonstrations supporting drills.
Guidance for families navigating junior tennis.
Connection to the existing progress-tracking experience.
Different tools, connected work
AI did a large amount of the implementation work, but we still had to decide what to build, test whether it worked, identify problems, and decide what should happen next.
Where we are now
Rally School is not intended to replace tennis coaches, clubs, parents, or the experience of learning on the court.
It is a small project created by a father and son who enjoy tennis, technology, and learning together. Generative AI helped us turn ideas into something real much faster than would have been possible for us before.
But perhaps the most important lesson was not how much AI could do for us. It was realizing how much we still needed to learn ourselves.
For Ethan and other young people growing up in the Generative AI era, knowing today's technology will not be enough, because technology will continue changing. What may matter even more is curiosity to explore something new, the ability to ask good questions, judgment to evaluate answers, willingness to learn from mistakes, persistence to keep improving, and the ability to use powerful tools responsibly.
You do not become a better tennis player from one perfect swing.
You practice. You make mistakes. You understand them. You adjust. You try again.
Learning with AI can work the same way.Keep playing. Keep building. Keep learning.
Built by David & Ethan for the junior tennis community.