How (Not) to Learn Python
While listening to Mark Smith’s inspirational talk for Python Unplugged on PyTV about How to Learn Python, what caught my attention was that Mark suggested turning off some of PyCharm’s AI features to help you learn Python more effectively.
As a PyCharm user myself, I’ve found the AI-powered features beneficial in my day-to-day work; however, I never considered that I could turn certain features on or off to customize my experience. This can be done from the settings menu under Editor | General | Code Completion | Inline.
While we are at it, let’s have a look at these features and investigate in more detail why they are great for professional developers but may not be ideal for learners.
Local full line code completion suggestions
JetBrains AI credits are not consumed when you use local line completion. The completion prediction is performed using a built-in local deep learning model. To use this feature, make sure the box for Enable inline completion using language models is checked, and choose either Local or Cloud and local in the options. To show the complete results using the local model alone, we will look at the predictions when only Local is selected.
When it’s selected, you see that the only code completion available out of the box in PyCharm is for Python. To make suggestions available for CSS or HTML, you need to download additional models.
When you are writing code, you will see suggestions pop up in grey with a hint for you to use Tab to complete the line.
After completing that line, you can press Enter to go to the next one, where there may be a new suggestion that you can again use Tab to complete. As you see, this can be very convenient for developers in their daily coding, as it saves time that would otherwise be spent typing obvious lines of code that follow the flow naturally.
However, for beginners, mindlessly hitting Tab and letting the model complete lines may discourage them from learning how to use the functions correctly. An alternative is to use the hint provided by PyCharm to help you choose an appropriate method from the available list, determine which parameters are needed, check the documentation if necessary, and write the code yourself. Here is what the hint looks like when code completion is turned off:
Cloud-based completion suggestions
Let’s have a look at cloud-based completion in contrast to local completion. When using cloud-based completion, next-edit suggestions are also available (which we will look at in more detail in the next section).
Cloud-based completion comes with support for multiple languages by default, and you can switch it on or off for each language individually.
Cloud-based completion provides more functionality than local model completion, but you need a JetBrains AI subscription to use it.
You may also connect to a third-party AI provider for your cloud-based completion. Since this support is still in Beta in PyCharm 2026.1, it is highly recommended to keep your JetBrains AI subscription active as a backup to ensure all features are available.
After switching to cloud-based completion, one of the differences I noticed was that it is better at multiple-line completion, which can be more convenient. However, I have also encountered situations where the completion provided too much for me, and I had to jump in to make my own modifications after accepting the suggestions.
For learners of Python, again, you may want to disable this functionality or have to audit all the suggestions in detail yourself. In addition to the danger of relying too heavily on code completion, which removes opportunities to learn, cloud code completion poses another risk for learners. Because larger suggestions require active review from the developer, learners may not be equipped to fully audit the wholesale suggestions they are accepting. Disabling this feature for learners not only encourages learning, but it can also help prevent mistakes.
Next edit suggestions
In addition to cloud-based completion, JetBrains AI Pro, Ultimate, and Enterprise users are able to take advantage of next edit suggestions.
When they are enabled, every time you make changes to your code, for example, renaming a variable, you will be given suggestions about other places that need to be changed.
And when you press Tab, the changes will be made automatically. You can also customize this behavior so you can see previews of the changes and jump continuously to the next edit until no more are suggested.
This is, no doubt, a very handy feature. It can help you avoid some careless mistakes, like forgetting to refactor your code when you make changes. However, for learners, thinking about what needs to be done is a valuable thought exercise, and using this feature can deprive them of some good learning opportunities.
Conclusion
PyCharm offers a lot of useful features to smooth out your day-to-day development workflow. However, these features may be too powerful, and even too convenient, for those who have just started working with Python and need to learn by making mistakes. It is good to use AI features to improve our work, but we also need to double-check the results and make sure that we want what the AI suggests.
To learn more about how to level up your Python skills, I highly recommend watching Mark’s talk on PyTV and checking out all the AI features that JetBrains AI has to offer. I hope you will find the perfect way to integrate them into your work while remaining ready to turn them off when you plan to learn something new.
