GraphQL

GraphQL Contributor Days | December 2021

Published Dec 21, 2021

Updated Feb 13, 2023

6 min read

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

This is a recap of the December GraphQL Contributor Days, an event where maintainers and contributors came together to talk about all things current and upcoming in the GraphQL realm! Read on or rewatch the event here to see what these experts have to share, and see what they're using at companies such as Facebook, PayPal, Hasura, and Netflix.

Our hosts for this event were Dustin Goodman and Tanmai Gopal.

Panelists

Adhithi Ravichandran, @AdhithiRavi
Conor Hastings @stillconor
Jamie Barton, @notrab
Joey Nenni, @JoeyNenni
Jon Wong, @jnwng
Lee Byron, @leeb
Marc-André Giroux, @__xuorig__
Roy Derks, @gethackteam
Tejas Kumar, @TejasKumar_
Uri Goldshtein, @UriGoldshtein
Kyle Schrade, @NotKyleSchrade
Patrick Arminio, @patrick91
Gabriel Nordeborn, @___zth___

Community Showcase & Updates

GraphQL Yoga (Uri Goldshtein)

GraphQL Yoga 2.0 has been in the works for the past few months, and it’s finally launched its alpha! This version is shipping with Envelop, and includes support for deferring streams, live queries, and the latest GraphiQL IDE.

RescriptRelay CLI (Gabriel Nordeborn)

A new feature has been added to the RescriptRelay CLI! This new command has the ability to go through your code and remove all unused fields and fragments from your GraphQL queries, which in turn helps prevent over-fetching in your application.

yarn rescript-relay-cli remove-unused-fields

GraphQL Learning

What can we do to support or easily onboard team members that may be new to GraphQL? What challenges do we face? Our panelists shared some of their favorite resources and methods that have worked for their teams, as well as brought up some great points about resources the community is still lacking.

The general consensus was that folks coming from REST can have trouble learning the GraphQL way of doing things. Things like introspection & HTTP 200 blanket responses can cause errors and introduce security risks that may be overlooked by those learning GraphQL for the first time.

Our panelists also agreed that there’s a need for concise resources outlining some of these common pitfalls, and there’s also a need for a simple, language-agnostic GraphQL starter package (think Create React App, but for GraphQL). Unfortunately, these resources don’t seem to exist just yet. However, our panelists did still have some great tips and resources to share for those trying to learn GraphQL.

Kyle Schrade had a couple suggestions that worked for his team:
- Create internal documentation where you can keep track of answers to common questions.
- Create a Codegen so that it’s easier for new team members to set up subgraphs.
Adhithi Rhavichandran shared a great Pluralsight course she co-authored on GraphQL API’s with Apollo. This is a great language-agnostic intro to GraphQL that talks about the differences between GraphQL and REST.
Jamie Barton created graphql.wtf to provide the GraphQL community with short, digestible videos on GraphQL concepts.

Authorization & Security

How can we handle authorization and security for our GraphQL API’s? What kinds of considerations need to be made? There is no “one size fits all” in handling authorization in GraphQL API’s.

With regards to where one should place their authorization logic, some argued for placing this logic at the resolver level (keeping auth logic close to business logic), while others wished to raise this logic up to a top layer to keep responsibilities separate. If the latter is your preference, Uri Goldshtein recommended GraphQL Authz for easily adding an authorization layer to your GraphQL API. This library is compatible with all modern GraphQL architectures as well.

As mentioned earlier, there were some differing ideas around whether or not to allow schema introspection. Though some saw it as a potential security risk, Joey Nenni argued that the benefits for developers can be enormous and provide a better user experience. If you've properly secured your API, there shouldn't be any significant risks involved with allowing schema introspection.

Federation

What are the benefits and challenges of federating? A pain point for Roy Derks is that underlying services need to be ready for federation. "It'd be nice if every schema could be federated with any schema," he said. Tanmai Gopal suggested that it'd be best to do configuration at the GraphQL entry point, rather than at the service level in order to tackle that problem.

What companies use federation, and how are they implementing it? Many of our panelists' companies are either actively using or planning to use Apollo Federation.

Tanmai Gopal, founder of Hasura, said that Hasura does federation in two steps - service to service federation, and data source to data source federation. At PayPal, Staff Engineer Joey Nenni shared that they're currently working on implementing the Apollo Federation spec.

Marc-André Giroux also shared that Netflix uses the Apollo Federation spec, though they have their own gateway built in Kotlin. The observability federation brings to their API, and the way federation fits within Netflix's microservice architecture works "beautifully" on his team. However, federating a monolith is perhaps not the best idea.

Lee Byron, co-creator of GraphQL, agreed and shared that Facebook has a different approach. "I think federation brings a lot of value, but at a lot of cost," he explained. Schema registries, query plans, and all of the other "stuff" that comes with federation can be a lot to manage.

"Facebook doesn’t use any of that ... instead they use a model ... where you have a single definition of your schema that exists at your gateway." - Lee Byron

Federation can be a great fit if your organization isn't a monolith and values team autonomy. "[At Facebook] team autonomy is an anti-pattern," Lee joked. Teams that value autonomy or that may not share a unified language, on the other hand, may find a lot of value in federating.

Other panelists agreed that company culture and team structure play a big part in determining whether to use a monolith or microservice structure, which can also determine whether or not federation is a good fit for your project. Culture can be very difficult to change, and may not even be possible.

"Regardless of the decision the net result is very similar. … However it is that we arrive at that conclusion, it doesn’t matter that much, and so we should choose the one that is easier for your organization." - Lee Byron

What's new at GraphQL Foundation?

The Working Group, which contributes changes to the GraphQL spec, is about to run a vote for the leaders of their technical steering committee. This steering committee will be responsible for overseeing the Working Group.
The GraphQL Foundation has a bunch of new members and sponsors, which has allowed them to launch a brand new community grant program in order to redistribute some of those funds and encourage community-driven development. You can find the grant program here!
There's a newly released cut of the GraphQL spec!

That's a Wrap!

GraphQL Contributor Days is always a great way to keep your ear to the ground on what's coming in the GraphQL ecosystem. This month's event provided some fascinating insight into how some of the top companies in the world are using GraphQL. If you'd like to watch the full event and hear our panelists dive deeper on some of these topics (and more), you can find it here!

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Star Richardson
Makeup artist turned self-taught software engineer and crypto enthusiast. Software Engineer at This Dot. Woman of a thousand interests and maker of many things.
@alicenstar @alicenstar

Class and Enum Typings for Handling Data with GraphQL

Intro Today we’ll talk about class and enum typings for handling more complex data with GraphQL. The typings will be used on class objects to make them easier to read and understand. This blog will build on the concepts of another blog found here, in which I discussed wrapping and enhancing data, which talks about how to create a GraphQL Rest API wrapper, and how to enhance your data. This article assumes you have a basic understanding of classes, typing, and enums. Here is the code repo if you want to review it while reading, or just skip ahead. With that said, we will look at the class structure we’re going to be using, the enums, and the type lookup for our customField enum with our mapper. Class setup This class we’re going to set up will be strictly for typing. We’re going to make a RestaurantGuest class as this data will be restaurant themed. So in our restaurant.ts file, we will name it RestaurantGuest, and include a few different items. *Basic starting class example* ` After setting that up, we will add a type that will reference the above class. *Type example* ` This type will be used later when we do the type lookup in conjunction with our mapper function. With the above finished, we can now move on to handling our enums. Handling enums We’ll now create our Enum to make dealing with our complex data easier. Since the above 3249258 represents FoodReservation, we will create an enum for it. Now you might be wondering why 3249258 represents FoodReservation. Unfortunately, this is an example of how data can be returned to us from a call. It could be due to the field id established in a CMS such as Contentful, or from another source that we don’t control. This isn’t readable, so we’re creating an Enum for the value. *Enum example* ` This will be used later during our type look-up. Type lookup Now we can start combining the enum from above with our class type, and a look-up type that we’re about to create. So let's make another type called RestaurantGuestFieldLookup, which will have an id and value. *Look-up type example* ` Perfect, now we’ll swap out ` to be ` We can now move on to creating and using our mapper. In a separate file called mapper.ts, we will create our restaruantGuestMapper function. ` Tada! Thanks to all the work above, we can easily understand and get back the data we need. Conclusion Today's article covered setting up a typing class, creating an enum, and type lookup with a mapper for more complex data handling with GraphQL. Hopefully, the class structure was straightforward and helpful, along with the enums and type lookup and formatting examples. If you want to learn more about GraphQL, read up on our resources available at graphql.framework.dev....

May 26, 2023

2 mins

GraphQL

How to create and use custom GraphQL Scalars

How to create and use custom GraphQL Scalars In the realm of GraphQL, scalars form the bedrock of the type system, representing the most fundamental data types like strings, numbers, and booleans. As explored in our previous post, "Leveraging GraphQL Scalars to Enhance Your Schema," scalars play a pivotal role in defining how data is structured and validated. But what happens when the default scalars aren't quite enough? What happens when your application demands a data type as unique as its requirements? Enter the world of custom GraphQL scalars. These data types go beyond the conventional, offering the power and flexibility to tailor your schema to precisely match your application's unique needs. Whether handling complex data structures, enforcing specific data formats, or simply bringing clarity to your API, custom scalars open up a new realm of possibilities. In this post, we'll explore how to understand, create, and effectively utilize custom scalars in GraphQL. From conceptualization to implementation, we'll cover the essentials of extending your GraphQL toolkit, empowering you to transform abstract ideas into concrete, practical solutions. So, let's embark together on the journey of understanding and utilizing custom GraphQL scalars, enhancing and expanding the capabilities of your GraphQL schema. Understanding Custom Scalars Custom scalars in GraphQL extend beyond basic types like String or Int, allowing data to be defined, validated, and processed more precisely. They're instrumental when default types don't quite capture the complexity or specificity of the data, such as with specialized date formats or unique identifiers. The use of custom scalars brings several benefits: * Enhanced Clarity: They offer a clearer representation of what data looks like and how it behaves. * Built-in Validation: Data integrity is bolstered at the schema level. * Flexibility: They can be tailored to specific data handling needs, making your schema more adaptable and robust. With this understanding, we'll explore creating and integrating custom scalars into a GraphQL schema, turning theory into practice. Creating a Custom Scalar Defining a Custom Scalar in TypeScript: Creating a custom scalar in GraphQL with TypeScript involves defining its behavior through parsing, serialization, and validation functions. * Parsing: Transforms input data from the client into a server-understandable format. * Serializing: Converts server data back to a client-friendly format. * Validation: Ensures data adheres to the defined format or criteria. Example: A 'Color' Scalar in TypeScript The Color scalar will ensure that every color value adheres to a valid hexadecimal format, like #FFFFFF for white or #000000 for black: ` In this TypeScript implementation: * validateColors: a function that checks if the provided string matches the hexadecimal color format. * parseValue: a method function that converts the scalar’s value from the client into the server’s representation format - this method is called when a client provides the scalar as a variable. See parseValue docs for more information * serialize: a method function that converts the scalar’s server representation format to the client format, see serialize docs for more information * parseLiteral: similar to parseValue, this method function converts the scalar’s value from the client to the server’s representation format. Still, this method is called when the scalar is provided as a hard-coded argument (inline). See parseLiteral docs for more information In the upcoming section, we'll explore how to incorporate and validate these custom scalars within your schema, ensuring they function seamlessly in real-world scenarios. Integrating Custom Scalars into a Schema Incorporating the 'Color' Scalar After defining your custom Color scalar, the next crucial step is effectively integrating it into your GraphQL schema. This integration ensures that your GraphQL server recognizes and correctly utilizes the scalar. Step-by-Step Integration 1. Add the scalar to Type Definitions: Include the Color scalar in your GraphQL type definitions. This inclusion informs GraphQL about this new scalar type. 2. Resolver Mapping: Map your custom scalar type to its resolver. This connection is key for GraphQL to understand how to process this type during queries and mutations. ` 1. Use the scalar: Update your type to use the new custom scalar ` Testing the Integration With your custom Color scalar integrated, conducting thorough testing is vital. Ensure that your GraphQL server correctly handles the Color scalar, particularly in terms of accepting valid color formats and rejecting invalid ones. For demonstration purposes, I've adapted a creation mutation to include the primaryColor field. To keep this post focused and concise, I won't detail all the code changes here, but the following screenshots illustrate the successful implementation and error handling. Calling the mutation (createTechnology) successfully: Calling the mutation with forced fail (bad color hex): Conclusion The journey into the realm of custom GraphQL scalars reveals a world where data types are no longer confined to the basics. By creating and integrating scalars like the Color type, we unlock precision and specificity in our GraphQL schemas, which significantly enhance our applications' data handling capabilities. Custom scalars are more than just a technical addition; they testify to GraphQL's flexibility and power. They allow developers to express data meaningfully, ensuring that APIs are functional, intuitive, and robust. As we've seen, defining, integrating, and testing these scalars requires a blend of creativity and technical acumen. It encourages a deeper understanding of how data flows through your application and offers a chance to tailor that experience to your project's unique needs. So, as you embark on your GraphQL journey, consider the potential of custom scalars. Whether you're ensuring data integrity, enhancing API clarity, or simply making your schema a perfect fit for your application, the possibilities are as vast as they are exciting. Embrace the power of customization, and let your GraphQL schemas shine!...

Apr 10, 2024

5 mins

GraphQLJavaScript

Benefits of Software Apprenticeship & How to Make the Most of Yours

It’s no surprise that over the past year, many have started switching careers into tech. I was one of thousands of people that hopped on the “Learn to Code” train last year after losing my retail job. Being without a college degree, I looked into programming because it was one of the few industries where you could teach yourself the necessary skills without a long and expensive college education. As with many things though, the process of learning to code, and finding a job, proved more difficult than expected. About two months into a fruitless job search, a mentor of mine told me about This Dot and their Apprentice Program that had helped her get started as a self-taught software engineer. I was so excited to find an approachable entry point into tech for self-taught folks like myself! What is an apprenticeship? So what is an apprenticeship, and how does it differ from an internship? As I understand it, an apprenticeship is a program that provides hands-on work experience with the explicit goal of placing you in a job. An internship is more focused on general exposure to a work environment, or industry, without the explicit goal of hiring or placing you at the end. Another major difference is that most internships will require you to be an active college student pursuing a degree, while apprenticeships are generally more accommodating of unconventional backgrounds. Why do an apprenticeship? These are some of the key reasons I decided to pursue an apprenticeship at This Dot Labs. Keep in mind that every apprenticeship and every company is different, and the reasons I’ve listed here are specific to my experience in This Dot’s Apprentice Program. 1. Mentorship Opportunities Before I started my apprenticeship at This Dot, I was told I would be assigned a mentor that would meet with me once or twice a week. I didn’t realize that nearly all of my teammates and managers would also become my mentors. If I have a question, someone on the team is bound to have the answer, and everyone is more than willing to help. I’m only a few weeks into the apprentice program, but the meetings I’ve had with my mentor, teammates, and managers have already helped grow my skills immensely. 2. Room to Learn and Grow The whole purpose of an apprenticeship is to give someone the room to ask questions, fail, learn, and grow. In my first week as an apprentice, I was absolutely terrified of asking too many “stupid” questions. I struggled trying to do things on my own. Over and over, my managers reassured and encouraged me to keep asking more. They understand what it’s like starting out as a dev in your first job, and they are there to help you through every struggle. I quickly learned that if I ask questions early on instead of struggling for hours, I actually end up getting more work done, and being a bigger help to my team. They strongly encourage you to follow your interests and curiosities outside of software development at This Dot as well. I’ve already been given opportunities to try out other areas of interest in tech, such as Developer Relations (DevRel). In my first few weeks, I’ve been able to do things I’ve never tried before, like speaking on a podcast, writing blog posts (like this one!) and tweets, and moderating a panel at MagnoliaJS Conference. This room to experiment is a rare and valuable opportunity for an early-career developer. 3. Hands-on Experience Working on high-impact projects for major companies, and actually being responsible for the outcomes of these projects, is incredibly rewarding and validating. Being given a high level of autonomy, and the opportunity to “rise to the occasion” will grow your skills faster than any internship where you might be given tasks unrelated to your area of interest. The opportunity to build confidence, and validate your skills so early in your career, is invaluable. 4. The Team This point is obviously specific to my experience at This Dot, but it is so important to find a company that meshes well with your personality and values. This Dot greatly values good people, good work, personal growth, and a diverse team. I’m exposed to a lot of different people with different backgrounds and unique perspectives at work, which has been immensely valuable. This Dot even has a *Hire the Fempire* program that works with companies to hire more women in tech through their apprentice program. 5. An Accessible Option This is perhaps the most important point to me and the reason that apprenticeships are so invaluable: They offer an easier way to break into the tech industry for people that otherwise might not have the resources to be able to pursue a degree. They allow people with unconventional backgrounds to get real job experience, and change the trajectory of their lives. How can I make the most of my apprenticeship? You've done it! You've made it past the barrier and gotten your first tech job! Here are some ways I plan to make the absolute most of my time as an apprentice. 1. Ask Questions Now is the time to ask a lot of questions, and when I say "a lot", I mean *a lot*. If you are like the majority of people who experience impostor syndrome, this is something you will need to push past very early on. It will only hold you back. If, like me, you are terrified of sounding "stupid" or being found out as a fraud, try to reframe how you view asking questions. You are not asking a stupid question. You are simply trying to get unblocked, to allow yourself to do more awesome work. Don't stifle your own progress because you're afraid of appearing less knowledgeable than you are. If you're still worried about overloading your teammates or mentors with lots of questions, try batching your questions. Keep a list of questions you'd like to ask, and ask several at a time rather than spreading them out throughout the day or week. Lastly, be sure you know how to ask a good question, and always show gratitude to the person who's helping you. 2. Go Out of Your Way to Meet People In the first few weeks, I made sure to set up a short video meeting with all my managers, teammates- anyone I would be working with directly. These are the people that will celebrate wins with you, and help you when you're struggling. Once you get to know people long enough, you'll probably start to see that everyone has a "thing" they're particularly passionate about. You'll find out who to go to for React or Angular help, who to go to for GraphQL or Elixir knowledge. Fostering good, genuine relationships is incredibly important if you want to have a meaningful career (and life). 3. Don't Hide Your Struggles If you're struggling, don't be afraid to ask for help. If you followed my last tip for building relationships, hopefully you'll have found who you can go to for emotional support. Talking out your struggles can be one of the quickest ways to move past them, and your confidant may have their own personal experiences that can help you with yours. 4. Stay Open to Criticism Feedback is the fastest way to level up as a developer and human being. To use feedback most effectively, it's important to separate yourself from your work. If someone offers you feedback that is delivered in a not-so-nice way, you can still take something constructive from it, and use it to improve your work. In a lot of development work, it can actually be hard to know how or what to improve, so I try to view criticism as a blessing in disguise, and be grateful for being given some direction. 5. Set Goals and Track Your Progress Speaking of direction, it's important to have goals to work towards in many aspects of life. Set some short and long term goals for your apprenticeship, and share them with your mentors. If your mentors are good mentors, they'll support you in achieving these goals. Along with this, it's also important to have some way to track your progress. Some people like to journal, some people keep a Google doc or a Notion page. Personally, I've been sending myself a weekly email to track my progress. Point is: find a way to track your progress, and keep up with it regularly. Closing Thoughts I hope this post was helpful and inspiring to anyone trying to break into the tech industry! My hope for the future is that more companies will adopt apprentice programs, and structure them in a way that empowers early-career devs to ask questions, fail fast and fail forward, and be fearless learners. Tech still has a major diversity problem, and a lot of marginalized groups fall into the category of those with unconventional career backgrounds. If more companies offered apprenticeships, it would be a great step in the right direction for bringing more diversity into tech....

Jun 10, 2021

6 mins

Career development

Increasing development velocity with Cursor

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: ` 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....

Apr 18, 2025

11 mins

AICursor

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GraphQL Contributor Days | December 2021

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Community Showcase & Updates

GraphQL Yoga (Uri Goldshtein)

RescriptRelay CLI (Gabriel Nordeborn)

GraphQL Learning

Authorization & Security

Federation

What's new at GraphQL Foundation?

That's a Wrap!

Star Richardson

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