There are different categories of tools we have to differentiate when it comes
to AI tools for developers. We have
external AI chatbots like ChatGPT and Gemini, for
example. Those are great for asking code-related
questions and getting answers or discussing general
concepts, not specifically to a concrete code base
might be working on but instead for a new project
planning where a lot of code-based specific knowledge is not
required, because, of course, ChatGPT and Gemini and all these other AI
chatbots don't have access to your code base unless you copy and paste
all those code files, which nobody wants to do.
That's where AI-powered completion comes into play as
a first in code-based tool. Here we have GitHub
Copilot and Cursor, which we'll both see in this course here,
and both these tools offer intelligent autocompletion.
That's not just completing based on type
definitions or anything like that but instead are context
aware of your file, the surrounding code of the
area you are currently working on, and that they offer...
Provide smart suggestions for what you might wanna type next.
And we'll take a look at these smart assistants and how they work later in the
course, of course. We'll also take a very close look at
coding agents, and here I'm talking about GitHub Copilot
Cursor. Another example which is not part of the course
course, would be Claude Code. Unlike the code completion,
here we're not just talking about completing parts of the code but
instead having the AI agent generate vast amounts of
codes, entire code files and working across multiple files in your
code base. These agents are aware of your
project. You can also give them specific extra
might need, and unlike ChatGPT and Gemini, they do work
in your code base and understand your code base,
significant amounts of codes with those agents, and as a developer, you wanna
learn how to control these agents.
And that's specifically what we'll also cover in this course, how to work
with Cursor and GitHub Copilot and how to provide the right
instructions to these agents. And then there
also would be code review tools, something like Bugbot by Cursor
or CodeRabbit or other tools. The idea here is that these are
tools that typically run in the cloud and automatically
review pull requests that might be opened on GitHub to have an
extra pair of AI eyes on that code and have some
automated code reviews being done for every pull request that's opened, for
example. And they therefore may catch and fix certain
issues or provide a solid baseline for human-based
reviews so that you can build up on what the AI already
identified. This will not be the focus of this course,
another important category of tools to be aware
of.