If you’re a developer, you’ve probably heard of Cursor by now and have either tried it out or are just curious to learn more about it. Cursor is a fork of VSCode with a ton of powerful AI/LLM-powered features added on. For around $20/month, I think it’s the best value in the AI coding space.
Tech giants like Shopify and smaller companies like This Dot Labs have purchased Cursor subscriptions for their developers with the goal of increased productivity.
I have been using Cursor heavily for a few months now and am excited to share how it’s impacted me personally. In this post, we will cover some of the basic features, use cases, and I’ll share some tips and tricks I’ve learned along the way.
If you love coding and building like me, I hope this post will help you unleash some of the superpowers Cursor’s AI coding features make possible. Let’s jump right in!
Cursor 101
The core tools of the Cursor tool belt are Autocomplete, Ask, and Agent.
Feature: Autocomplete
The first thing that got me hooked was Autocomplete. It just worked so much better than the tools I had used previously, like GitHub Copilot. It was quicker and smarter, and I could immediately notice the amount of keystrokes that it was saving me.
This feature is great because it doesn’t really require any work or skilled prompting from the user. There are a couple of tricks for getting a little bit more out of it that I will share later, but for now, just enjoy the ride!
Feature: Ask
If you’ve interacted with AI/LLMs before, like ChatGPT - this is what the Ask feature is. It’s just a chat feature you can easily provide context to from your code base and choose which Model to chat with.
This feature is best suited for just asking more general questions that you might have queried Google or Stack Overflow for in the past. It’s also good for planning how to implement a feature you’re working on.
After chatting or planning, you can switch directly to Agent mode to pick up and take action on something you were cooking up in Ask mode.
Here’s an example of planning a simple tic-tac-toe game implementation using the Ask feature:
Feature: Agent
Agent mode lets the AI model take the wheel and write code, make edits, or take other similar actions on your code base. The goal is that you can write prompts and give instructions, and the Agent can generate the code and build features or even entire applications for you.
With great power comes great responsibility.
Agents are a feature where the more you put into them, the more you get out. The more skilled you become in using them by providing better prompts and including the right context, you will continue to get better results.
The AI doesn’t always get it right, but the fact that the models and the users are both getting better is exciting. Throughout this post, I will share the best use cases, tips, and tricks I have found using Cursor Agent.
Here’s an example using the Agent to execute the implementation details of the tic-tac-toe game we planned using Ask:
Core Concept: Context
After understanding the features and the basics of prompting, context is the most important thing for getting the best results out of Cursor.
In Cursor and in general, whenever you’re prompting a chat or an agent, you want to make sure that it has all the relevant information that it needs to provide an answer or result.
Cursor, by default, always has some context of your code. It indexes your code base and usually keeps the open buffer in the context window at the very least.
At the top left of the Ask or Agent panel, there is an @ button, and next to that are badges for all the current items that have been explicitly added to the context for the current session. The @ button has a dropdown that allows you to add files, folders, web links, past chats, git commits, and more to the context.
Before you prompt, always make sure you add the relevant content it needs as context so that it has everything it needs to provide the best response.
Settings and Rules
Cursor has its own settings page, which you can access through Cursor → Settings → Cursor Settings.
This is where you log in to your account, manage various features, and enable or disable models.
In the General section, there is an option for Privacy Mode. This is one setting in particular I recommend enabling. Aside from that, just explore around and see what’s available.
Models
The model you use is just as important as your prompt and the context that you provide. Models are the underlying AI/LLM used to process your input. The most well-known is GPT-4o, the default model for ChatGPT. There are a lot of different models available, and Cursor provides access to most of them out of the box.
Model pricing
A lot of the most common models, like GPT-4o or Sonnet 3.5/3.7, are included in your Cursor subscription. Some models like o1 and Sonnet 3.7 MAX are considered premium models, and you will be billed for usage for these.
Be sure to pay attention to which models you are using so you don’t get any surprise bills.
Choosing a Model
Some models are better suited for certain tasks than others. You can configure which models are enabled in the Cursor Settings.
If you are planning out a big feature or trying to solve some complex logic issue, you may want to use one of the thinking models, like o1, o3-mini, or Deep Seek R1.
For most coding tasks and as a good default, I recommend using Sonnet 3.5 or 3.7.
The great thing about Cursor is that you have the options available right in your editor. The most important piece of advice that I can give in this post is to keep trying things out and experimenting. Try out different models for different tasks, get a feel for it, and find what works for you.
Use cases
Agents and LLM models are still far from perfect. That being said, there are already a lot of tasks they are very good at. The more effective you are with these tools, the more you will be able to get done in a shorter amount of time.
Generating test cases
Have some code that you would like unit tested? Cursor is very good at generating test cases and assertions for your code. The fewer barriers there are to testing a piece of code, the better the result you will get. So, try your best to write code that is easily testable! If testing the code requires some mocks or other pieces to work, do your best to provide it the context and instructions it needs before writing the tests.
Always review the test cases! There could be errors or test cases that don’t make sense. Most of the time, it will get you pretty close to where you want to be.
Here’s an example of using the Agent mode to install packages for testing and generate unit tests for the tic-tac-toe game logic:
Generating documentation
This is another thing we know AI models are good at - summarizing large chunks of information. Make sure it has the context of whatever you want to document.
This one, in particular, is really great because historically, keeping documentation up to date is a rare and challenging practice.
Here’s an example of using the Agent mode to generate documentation for the tic-tac-toe game:
Code review
There are a lot of up-and-coming tools outside of Cursor that can handle this. For example, GitHub now has Copilot integrated in pull requests for code reviews. It’s never a bad idea to have whatever change set you’re looking to commit reviewed and inspected before pushing it up to the remote, though. You can provide your unstaged changes or even specific commits as context to a Cursor Ask or Agent prompt.
Getting up to speed in a new code base
Being able to query a codebase with the power of LLM’s is truly fantastic. It can be a great help to get up to speed in a large new codebase quickly.
Some example prompts:
Please provide an overview of this project and how to get started developing with it
I need to make some changes to the way that notifications are grouped in the UI, please provide a detailed analysis and pseudo code outlining how the grouping algorithm works
If you have a question about the code base, ask Cursor!
Refactoring
Refactoring code in a code base is a much quicker process in Cursor. You can execute refactors depending on their scope in a couple of distinct ways.
For refactors that don’t span a lot of files or are less complex, you can probably get away with just using the autocomplete. For example, if you make a change to something in a file and there are several instances of the same pattern following, the autocomplete will quickly pick up on this and help you tab through the changes. If you switch to another file, this information will still be in context and can be continued most of the time.
For larger refactors spanning several files, using the Agent feature will most likely be the quickest way to get it done.
Add all the files you plan to make changes to the Agent tab’s context window.
Provide specific instructions and/or a basic example of how to execute the refactor.
Let the Agent work, if it doesn’t get it exactly right initially, you can always give it corrections in a follow-up prompt.
Generating new code/features
This is the big promise of AI agents and the one with the most room for mixed results. My main recommendation here is to keep experimenting. Keep learning to prompt more effectively, compare results from different models, and pay attention to the results you get from each use case.
I personally get the best results building new features in small, focused chunks of work. It can also be helpful to have a dialog with the Ask feature first to plan out the feature's details that the Agent can follow up on and implement.
If there are existing patterns in your codebase for accomplishing certain things, provide this information in your prompts and make sure to add the relevant code to the context. For example, if you’re adding a new form to the web page and you have other similar forms that handle validation and making back-end calls in the same way, Cursor can base the code for the new feature on this.
Example prompt: Generate a form for creating a new post, follow similar patterns from the create user profile form, and look to the post schema for the fields that should be included.
Remember that you can always follow up with additional prompts if you aren’t quite happy with the results of the first.. If the results are close but need to be adjusted in some way, let the agent know in the next prompt.
You may find that for some things, it just doesn’t do well yet. Mentally note these things and try to get to a place where you can intuit when to reach for the Agent feature or just write some of the code the old-fashioned way.
Tips and tricks
The more you use Cursor, the more you will find little ways to get more out of it. Here are some of the tips and patterns that I find particularly useful in my day-to-day work.
Generating UI with screenshots
You can attach images to your prompts that the models can understand using computer vision. To the left of the send button, there is a little button to attach an image from your computer.
This functionality is incredibly useful for generating UI code, whether you are giving it an example UI as a reference for generating new UI in your application or providing a screenshot of existing UI in your application and prompting it to change details in reference to the image.
Cursor Rules
Cursor Rules allow you to add additional information that the LLM models might need to provide the best possible experience in your codebase. You can create global rules as well as project-specific ones.
An example use case is if your project has some updated dependency with newer APIs than the one on which the LLM has been trained. I ran into this when adding Tailwind v4 to a project; the models are always generating code based on Tailwind v3 or earlier.
Here’s how we can add a rules file to handle this use case:
Path: .cursor/rules/tailwind.mdc
Rule Type: Agent Requested
Description: When generating CSS using Tailwind utility classes
Access CSS variables in Tailwind CSS using the text(--var-name) syntax and not the previous text-[var(--var-name)] syntax
If you want to see some more examples, check out the awesome-cursorrules repository.
Summary
Learn to use Cursor and similar tools to enhance your development process. It may not give you actual superpowers, but it may feel like it. All the features and tools we’ve covered in this post come together to provide an amazing experience for developing all types of software and applications.
