I heard this feeling repeated several times:
One thing about this thinking is not to sit on me for a variety of reasons. For one, the possibility of a large language model (LLM) or agent successfully completes such tasks low. You can meet, “Well, if I get 70% on the road there, which can be valuable.” And on that, I will talk about. However, what happens to the next gifts is a greater, and mostly unsteady, problem. That brings me to my second concern, which is more consequence than dealing with the subpar output from an AI agent. And it all boils feedback, learning opportunities, and incentive structures.
LLMS is not learning
The only way to improve as a programmer is to write more tests in different ways, experimentation, failure, and learning. Progress as a developer fully regarding “time of stick.” It’s about the struggle to fix an issue of 50 seems different ways, to just know that you call the wrong function. These small, disappointing moments, accompanied by guidance from senior developers, add a lot of time to make a junior programmer’s career meaningful. These experiences open opportunities and, finally, making life growth that changes.
But here is the matter: LLMS cannot learn. They don’t have a career tails. They do not grow or improve from their “code struggle” how people do. Small-earned llms can get expensive expensive and repairs or tuning, not the type of instant tailor you get from a person learning work. Cause, the feedback loop for a llm is greater and more complicated. Sure, you can increase an LLM by adding the items to its extreme. But the greater context you’ve added, the less space you left for reasoning. You can write a blog post or documentation as feedback, but there is no guarantee that the LLM provider is also scraining in the room – or that it will make a difference between data.
On the contrary, if you give feedback to someone developing a review code, they can then work it, internalize it, and apply it ahead. This feedback loop is precious, immediate, and more affected than one with a AI model can.
Using a LLM cannot help anyone
There is something to rewarding about the teaching process for the student and also the teacher. Helping a developer reach the solution, or even giving them the opportunity to try to go to a solution, can lead to growth. As a senior developer, that can be a great reason to inspire for why you keep doing what you do. At the end of the day, Chatgpt or Claude Claude implemented, leaving the senior developer with no significant experience in the outsens of llm.
One more thing you can say: “Good if senior devs use LLMS not also available to junior developers?” And so I say develops their opportunities to learn social skills. The senior developer may use it as a place to learn to teach while junior is available as another opportunity to learn to accept useful feedback. These skills can be more valuable to the whole two of their races. In the same root, the eliminations of a true problem with LLMS. Juniors lack the experience to say what is true and what is not from a LLM. That would be a dangerous problem and there is no obvious solution today.
In the end it can damage the company as well. Training juniors and get them to shipping code as early as possible has a huge impact on their trajectory to a company. They feel them owning machinery that gives business value as soon as possible. Do not allow tasks to go to an LLM. Allow this junior. They will be proud of their work. Allow them to do what they do best: Learn.
A great way of going
Well, where are we from here? As senior developers, we need to be more intentional about how we can hand out the tasks.
For extraordinary or challenging tasks, give it to junior developers or interns. They are they who get the most from dealing with something new. On the other hand, repeatedly or mundane tasks all the team did many times? These are better candidates for automation, by AI (to say that this is the job dependent) or a deterministic script.
This method is not only practical – it should. While AI tools can be more than our workflows, the temptation to trust them will grow. But we must prevent the prompting of sacrifice and development of junior in favor of convenience. If we fail to invest in our juniors, the gap between beginners and seniors will raise, and it is harder for new developers. We have to work to smooth this curve in learning – it’s not steepen.
Finally
The future of our industry depends on how we choose to invest in our people. Junior developers are tech lifeblood-they will ultimately solve tomorrow’s problems. By empowering them to make meaningful tasks and learn from experienced companions, we make sure not only their growth, but long success throughout the field.
So the next time you give the tasks, think carefully. Instead of reaching for an LLM to solve that part or repair, think about giving it to a junior developer. Teach them. Teach them. Help them learn. Sharing payments are more of the more aware recovery – and you can find happiness in the process.
We cannot allow AI to steal junior developers in opportunities they need to grow. However, we will give the way for the next generation. Let’s invest our future.
2025-02-03 07:39:00
