In recent years, artificial intelligence has become an integral part of software development. Tools like Cursor or GitHub Copilot allow developers to write code faster, analyze bugs and streamline workflows. For junior developers in particular, this presents new opportunities, but also challenges - such as fears of becoming too dependent on these tools or losing a deeper understanding of programming.
I first encountered artificial intelligence during my studies at the Faculty of Information Technology at CTU, where I studied knowledge engineering (AI). At that time, I worked with then quite modern NLP models such as BERT and explored their use, for example, in classifying the relationship between sentences. I admit that I was rather sceptical about the growing confidence in large language models (LLMs).
The real breakthrough came when I started my first job, where I started using tools like Cursor on a regular basis. These gradually showed me that AI can not only be an effective tool, but also a means to gain a deeper understanding of programming concepts and support career growth.
In this article I would like to share my experience with using AI in development from the perspective of a junior developer. I will focus on the advantages and disadvantages of these tools and offer practical tips that help me in my daily work.
Acceleration of learning
AI tools like Cursor or GitHub Copilot give junior developers instant feedback on their code. This allows them to identify and fix bugs faster, without having to wait for help from more experienced colleagues. In addition, these tools can explain complex programming concepts in simpler language, tailored to the user's level of expertise, which speeds up the learning process and helps to better understand basic and more advanced programming principles.
Increase productivity
Automating routine tasks such as generating boilerplate code (code that can be reused with little or no changes in different contexts), writing tests, or refactoring is another major advantage of AI tools. This allows junior developers to focus more on solving more complex problems and the creative aspects of development. Studies show that using AI tools can increase developer productivity by up to 55 %leading to faster task completion and a more efficient workflow.
Improving code quality
AI tools help identify potential errors in code, suggest (with proper reference) better practices, and promote adherence to conventions. In doing so, they help to improve code quality and reduce (in theory) the likelihood of errors that could lead to production problems. For example, GitHub Copilot allows developers to complete tasks about 26% faster, indicating not only an increase in productivity, but also an improvement in the quality of the resulting code.
Overcoming barriers
Junior developers may find it difficult to understand foreign code, documentation or technical articles. AI tools can help overcome these barriers by providing translations, summaries and explanations of complex concepts, making it easier to navigate new technologies and projects.
Inspiration and new ideas
AI can serve as a source of inspiration, generating suggestions for solutions and alternative approaches to problems. This supports and helps junior developers to develop their skills in finding new solutions that they would not have come up with on their own.
Suppression of basic skills
Over-reliance on AI tools can lead to a weakening of basic programming skills. Junior developers who rely too heavily on AI to write code may neglect a deeper understanding of algorithms, data structures, and software design principles. This trend can lead to superficial learning, where developers are unable to explain how and why their code works, which can be problematic when tackling more complex tasks.
Reduced ability to solve problems independently
AI tools can weaken the ability of junior developers to solve problems independently. With frequent use of AI for everyday tasks, critical thinking and creativity, which are key skills for effective software development, can decline. This phenomenon, referred to as "cognitive overload", can lead to developers shifting their thinking to AI tools, weakening their ability to make independent decisions.
Potential inaccuracies and errors
AI tools are not infallible and may generate code with bugs or security vulnerabilities. For example, the NYU study found that GitHub Copilot generated code with security vulnerabilities in cases. Junior developers should be able to critically assess AI outputs and not accept them without a thorough review.
Dependence on tools
Over-reliance on specific AI tools can be limiting, especially if those tools change or become unavailable. This dependence can lead to problems adapting to new technologies or work environments where the tools are not available.
Safety and ethical issues
The use of AI can raise questions about data protection, licensing of AI-generated code, and ethical implications. AI tools may generate code containing unless the developer properly checks and understands it. For example, the AI may suggest the use of outdated libraries or code that does not conform to the architectural patterns of a particular project.
Impact on employers and teams
It is important that junior developers use AI tools as a supplement to their own learning and development, not as a substitute for deeper understanding and hands-on experience. Critical thinking, independent problem solving, and thorough review of generated code are key to safe and effective software development.
Generating boilerplate code
AI tools allow junior developers to quickly generate basic structures for projects, components or functions. This reduces the time spent repeatedly writing routine code and allows them to focus on solving more complex problems. For example, Cursor, built on Visual Studio Code, offers advanced AI capabilities in a familiar environment, making it easier to adapt to new projects.
Writing and improving tests
AI tools can help generate various forms of automated tests, including unit tests and integration tests. This not only helps juniors learn best testing practices, but also improves overall code coverage and quality. for example, offers AI-powered test generation features, making it easy to implement thorough testing strategies.
Refactoring and code optimization
AI tools can suggest better code structure or identify opportunities for performance optimization. For example, GitHub Copilot and Cursor provide suggestions for refactoring code, which helps keep code clean and efficient. These tools can also identify outdated libraries or inefficient parts of the code that could be optimized.
Explaining complex code
AI can help junior developers understand foreign or complex code. AI tools offer features for explaining code, identifying design patterns, and clarifying the logic of individual pieces of code. This is particularly useful when engaging in new projects or working with legacy code.
Generating documentation
AI can help with automatic generation of code documentation, including comments, API documentation, and other forms of technical documentation.
Finding and understanding error messages
AI tools can help interpret and resolve error messages.
Learning new technologies
AI can provide a quick overview and explanation of new technologies and concepts. These tools can help junior developers understand new frameworks, libraries, or programming languages by providing code examples and a level of explanation based on the developer's experience.
Critical thinking and validation of AI outputs
Artificial intelligence is not infallible. It is therefore essential that junior developers always verify the accuracy, quality and relevance of the output generated by AI. AI can sometimes design inefficient, outdated or even erroneous code. Critical review of designs and self-testing is crucial to ensure applications function properly and for professional growth.
Occasional minimization of AI use
Similar to studying for exams, we often gain more by trying to write certain things ourselves - without the help of materials or AI tools. Learning is not always a pleasant process; we make mistakes at first and feel like we're getting nowhere. Over time, however, we begin to reap the rewards of our own efforts, and the knowledge becomes more solidified. With this experience, we can then make better use of outside help - because we ourselves will be the ones who understand, decide and create. Our code is ultimately our calling card, even if AI helped us with parts of it.
Using AI as an assistant, not a replacement for yourself
AI should be seen as a tool that augments the developer's skills, not as a replacement. Juniors should avoid relying on AI as a shortcut to a ready-made solution, and instead use it to better understand the problem, as an aid to learning new techniques, or to solve specific problems.
Focus on understanding the basics
Even when using AI, it is important not to underestimate the study of basic programming principles. Juniors should explore how and why AI designed specific solutions and seek a deeper understanding of algorithms, data structures, application architecture, and other key concepts.
Experimenting and learning from mistakes
The use of AI tools should also be an opportunity for experimentation. Juniors should not be afraid to test different approaches suggested by AI, try alternative solutions and learn from any mistakes. In this way, they are not only effectively learning to use the tools, but also developing their own skills.
Using AI to learn new things
AI can be a powerful tool for discovering new technologies and concepts. By querying or tasking tools such as GitHub Copilot, juniors can quickly develop an understanding of a new framework, library or methodology and then dive deeper into the topic.
Communication and cooperation
Even the best AI tool is no substitute for communication with more experienced colleagues, mentors or team. Sharing experiences, discussing approaches and feedback are essential for junior growth. AI should support collaboration, not replace it.
Expectations: realistic approach to AI tools
It is important to have realistic expectations of AI capabilities. Juniors should understand that AI has limitations - for example, it may not understand the specifics of a particular project, it may not know the current business context, and its outputs should always be evaluated in a specific situation. AI should be seen as a helper, not an authority.
Using AI in development brings significant benefits to junior developers - faster learning, easier navigation of complex concepts, increased productivity and instant feedback. AI tools can help with code generation, testing, documentation and refactoring. But at the same time, risks such as over-reliance on AI, superficial understanding of principles, or uncritical adoption of designs cannot be overlooked. These threats can lead to a loss of developer skills.
Therefore, it is crucial that juniors approach AI tools with discernment. They should see them as a supplement/tool to streamline their skills and knowledge acquisition - not as a replacement. Critical thinking, outcome validation, and continuous learning are essential for non-embeddedness.
The balance between AI assistance and actively developing one's own skills is the basis for effective use of these technologies. Going forward, the role of AI in software development can be expected to continue to grow. Those who learn to use AI tools correctly and responsibly will have an advantage not only in learning but also in building their careers. Employers should therefore adjust their assessment methods for junior developers to reflect new competencies, such as the ability to collaborate with AI or critically assess its outputs.
In conclusion, it is important to stress that AI can be a great tool for junior developers - if used with intelligence, curiosity and responsibility.
With the right guidance and a willingness to learn, juniors can turn AI into a powerful partner.
I am a software engineer passionate about web development, AI, and optimisation. I have experience with TypeScript, Next.js, Python, and several other languages. I'm always happy to learn new technologies that solve real-world challenges.
How does Netflix know what you want to watch before you do?
How Netflix's recommendation system works.
What is the Mixture of Experts (MoE) and why is it being talked about now?
Mixture of Experts could solve one of the biggest problems of current AI - how to scale models without costing a rocket or requiring a datacenter the size of a city.
Have you heard the term vibe coding? Vibe coding is a way of creating applications through AI where prompting is used instead of manually writing code.
TL;DR of the most commonly used AI terms
Getting lost in the terminology of the AI world? Then this article is for you. We've put together the most searched and most used terms related to AI.