GraphQL

Introducing the New Serverless, GraphQL, Apollo Server, and Contentful Starter kit

Published Jun 2, 2023

Updated Jun 2, 2023

7 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This article was written over 18 months ago and may contain information that is out of date.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.

Introducing the new Serverless, GraphQL, Apollo Server, and Contentful Starter kit

The team at This Dot Labs has released a brand new starter kit which includes the Serverless Framework, GraphQL, Apollo Server and Contentful configured and ready to go. This article will walk through how to set up the new kit, the key technologies used, and reasons why you would consider using this kit.

How to get started setting up the kit
How to Create the Technology Model in Contentful
How to seed the database with demo data
How to work with the migration scripts
Technologies included in this starter kit
Why use the Jest testing framework?
Project structure
How to deploy your application
What can this starter kit be used for?
Conclusion

How to get started setting up the kit

Generate the project

In the command line, you will need to start the starter.dev CLI by running the npx @this-dot/create-starter command. You can then select the Serverless Framework, Apollo Server, and Contentful CMS kit and name your new project. Then you will need to cd into your new project directory and install the dependencies using the tool of your choice (npm, yarn, or pnpm). Next, you will need to Run cp .env.example .env to copy the contents of the .env.example file into the .env file.

Setup Contentful access

You will first need to create an account on Contentful, if you don't have one already. Once you are logged in, you will need to create a new space. From there, go to Settings -> API keys and click on the Content Management Tokens tab. Next, click on the Generate personal token button and give your token a name. Copy your new Personal Access Token, and add it to the CONTENTFUL_CONTENT_MANAGEMENT_API_TOKEN variable. Then, go to Settings -> General settings to get the CONTENTFUL_SPACE_ID. The last step is to add those CONTENTFUL_CONTENT_MANAGEMENT_API_TOKEN and CONTENTFUL_SPACE_ID values to your .env file.

Setting up Docker

You will first need to install Docker Desktop if you don't have it installed already. Once installed, you can start up the Docker container with the npm run infrastructure:up command.

Starting the local server

While the Docker container is running, open up a new tab in the terminal and run npm run dev to start the development server. Open your browser to http://localhost:3000/dev/graphql to open up Apollo server.

How to Create the Technology Model in Contentful

To get started with the example model, you will first need to create the model in Contentful.

Log into your Contentful account
Click on the Content Model tab
Click on the Design your Content Modal button if this is your first modal
Create a new model called Technology
Add three new text fields called displayName, description and url
Save your new model

How to seed the database with demo data

This starter kit comes with a seeding script that pre-populates data for the Technology Content type.

In the command line, run npm run db:seed which will add three new data entries into Contentful.

If you want to see the results from seeding the database, you can execute a small GraphQL query using Apollo server.

First, make sure Docker, and the local server(npm run dev) are running, and then navigate to http://localhost:3000/dev/graphql.

Add the following query:

query TechnologyQuery {
  technologies {
    description
    displayName
    url
  }
}

When you run the query, you should see the following output.

{
  "data": {
    "technologies": [
      {
        "description": "GraphQL provides a strong-typing system to better understand and utilize our API to retrieve and interact with our data.",
        "displayName": "GraphQL",
        "url": "https://graphql.framework.dev/"
      },
      {
        "description": "Node.js® is an open-source, cross-platform JavaScript runtime environment.",
        "displayName": "Node.js",
        "url": "https://nodejs.framework.dev/"
      },
      {
        "description": "Express is a minimal and flexible Node.js web application framework.",
        "displayName": "Express",
        "url": "https://www.npmjs.com/package/express"
      }
    ]
  }
}

How to work with the migration scripts

Migrations are a way to make changes to your content models and entries. This starter kit comes with a couple of migration scripts that you can study and run to make changes to the demo Technology model. These migration scripts are located in the scripts/migration directory.

To get started, you will need to first install the contentful-cli.

   npm i -g contentful-cli

You can then login to Contentful using the contentful-cli.

   contentful login

You will then need to choose the Contentful space where the Technology model is located.

   contentful space use

If you want to modify the existing demo content type, you can run the second migration script from the starter kit.

   contentful space migration scripts/migrations/02-edit-technology-contentType.js -y

If you want to build out more content models using the CLI, you can study the example code in the /scripts/migrations/01-create-technology-contentType.js file. From there, you can create a new migration file, and run the above contentful space migration command.

If you want to learn more about migration in Contentful, then please check out the documentation.

Technologies included in this starter kit

Why use GraphQL?

GraphQL is a query language for your API and it makes it easy to query all of the data you need in a single request. This starter kit uses GraphQL to query the data from our Contentful space.

Why use Contentful?

Contentful is a headless CMS that makes it easy to create and manage structured data. We have integrated Contentful into this starter kit to make it easy for you to create new entries in the database.

Why use Amazon Simple Queue Service (SQS)?

Amazon Simple Queue Service (SQS) is a queuing service that allows you to decouple your components and process and store messages in a scalable way.

In this starter kit, an SQS message is sent by the APIGatewayProxyHandler using the sendMessage function, which is then stored in a queue called DemoJobQueue. The SQS handler sqs-handler polls this queue, and processes any message received.

import { APIGatewayProxyHandler } from "aws-lambda";
import { sendMessage } from "../utils/sqs";

export const handler: APIGatewayProxyHandler = async (event) => {
  const body = JSON.parse(event.body || "{}");
  const resp = await sendMessage({
    id: Math.ceil(Math.random() * 100),
    message: body.message,
  });

  return {
    statusCode: resp.success ? 200 : 400,
    body: JSON.stringify(resp.data),
  };
};

Why use Apollo Server?

Apollo Server is a production-ready GraphQL server that works with any GraphQL client, and data source. When you run npm run dev and open the browser to http://localhost:3000/dev/graphql, you will be able to start querying your Contentful data in no time.

Why use the Serverless Framework?

The Serverless Framework is used to help auto-scale your application by using AWS Lambda functions. In the starter kit, you will find a serverless.yml file, which acts as a configuration for the CLI and allows you to deploy your code to your chosen provider.

This starter kit also includes the following plugins:

serverless-offline - allows us to deploy our application locally to speed up development cycles.
serverless-plugin-typescript - allows the use of TypeScript with zero-config.
serverless-dotenv-plugin - preloads function environment variables into the Serverless Framework.

Why use Redis?

Redis is an open-source in-memory data store that stores data in the server memory. This starter kit uses Redis to cache the data to reduce the API response times and rate limiting. When you make a new request, those new requests will be retrieved from the Redis cache.

Why use the Jest testing framework?

Jest is a popular testing framework that works well for creating unit tests.

You can see some example test files under the src/schema/technology directory. You can use the npm run test command to run all of the tests.

Project structure

Inside the src directory, you will find the following structure:

.
├── generated
│   └── graphql.ts
├── handlers
│   ├── graphql.ts
│   ├── healthcheck.spec.ts
│   ├── healthcheck.ts
│   ├── sqs-generate-job.spec.ts
│   ├── sqs-generate-job.ts
│   ├── sqs-handler.spec.ts
│   └── sqs-handler.ts
├── models
│   └── Technology
│       ├── create.spec.ts
│       ├── create.ts
│       ├── getAll.spec.ts
│       ├── getAll.ts
│       ├── getById.spec.ts
│       ├── getById.ts
│       ├── index.ts
│       ├── TechnologyModel.spec.ts
│       └── TechnologyModel.ts
├── schema
│   ├── technology
│   │   ├── index.ts
│   │   ├── technology.resolver.spec.ts
│   │   ├── technology.resolvers.ts
│   │   └── technology.typedefs.ts
│   └── index.ts
└── utils
    ├── contentful
    │   ├── contentful-healthcheck.spec.ts
    │   ├── contentful-healthcheck.ts
    │   ├── contentful.spec.ts
    │   ├── contentful.ts
    │   └── index.ts
    ├── redis
    │   ├── index.ts
    │   ├── redis-healthcheck.spec.ts
    │   ├── redis-healthcheck.ts
    │   ├── redis.spec.ts
    │   └── redis.ts
    ├── sqs
    │   ├── client.spec.ts
    │   ├── client.ts
    │   ├── getQueueUrl.spec.ts
    │   ├── getQueueUrl.ts
    │   ├── index.ts
    │   ├── is-offline.spec.ts
    │   ├── is-offline.ts
    │   ├── sendMessage.spec.ts
    │   └── sendMessage.ts
    └── test
        └── mocks
            ├── contentful
            │   ├── entry.ts
            │   └── index.ts
            ├── aws-lambda-handler-context.ts
            ├── graphql.ts
            ├── index.ts
            └── sqs-record.ts

This given structure makes it easy to find all of the code and tests related to that specific component. This structure also follows the single responsibility principle which means that each file has a single purpose.

How to deploy your application

The Serverless Framework needs access to your cloud provider account so that it can create and manage resources on your behalf. You can follow the guide to get started.

Steps to get started:

Sign up for an AWS account
Create an IAM User and Access Key

Export your AWS_ACCESS_KEY_ID & AWS_SECRET_ACCESS_KEY credentials.

   export AWS_ACCESS_KEY_ID=<your-key-here>
   export AWS_SECRET_ACCESS_KEY=<your-secret-key-here>

Deploy your application on AWS Lambda:
```
   npm run deploy
```

To deploy a single function, run:

   npm run deploy function --function myFunction

To stop your Serverless application, run:

   serverless remove

For more information on Serverless deployment, check out this article.

What can this starter kit be used for?

This starter kit is very versatile, and can be used with a front-end application for a variety of situations.

Here are some examples:

personal developer blog
small e-commerce application

Conclusion

In this article, we looked at how we can get started using the Serverless, GraphQL, Apollo Server, and Contentful Starter kit. We also looked at the different technologies used in the kit, and why they were chosen. Lastly, we looked at how to deploy our application using AWS.

I hope you enjoy working with our new starter kit!

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)

Jessica Wilkins
Jessica Wilkins is a classical musician turned Software Engineer. Prior to joining the tech industry, she spent her time running her own sheet music company (JDW Sheet Music) as well as performing and teaching in Los Angeles, CA. She enjoys working with React and TypeScript. She is also a prolific technical writer for freeCodeCamp.
@codergirl1991 @jdwilkin4

Efficiently Extract Object References in Shopify Storefront GraphQL API

Efficiently Extract Object References in Shopify Storefront GraphQL API Introduction So, this blog post is born out of necessity and a bit of frustration. If you're diving into the world of Shopify's Storefront API, you've probably realized that while it's powerful, extracting data in the object reference from Metadata fields or Metaobjects (in the GraphQL query) can be a bit like searching for a needle in a haystack. This complexity often arises not from the API's lack of capabilities but from the sparse and sometimes unclear documentation on this specific aspect. That's precisely why I decided to create this post. As a developer, I found myself in a situation where the documentation and community resources were either scarce or not detailed enough for the specific challenges I faced. This guide is the result of my journey - from confusion to clarity. The Situation To understand the crux of my challenge, it's essential to recognize that creating metafields and metaobjects is a common practice for those seeking a more customized and controlled experience with Shopify CMS. In my specific case, I wanted to enrich the information available for each product's vendor beyond what Shopify typically allows, which is just a single text box. I aimed to have each vendor display their name and two versions of their logo: a themed logo that aligns with my website's color scheme and an original logo for use on specific pages. The challenge emerged when I fetched a list of all vendors to display on a page. My GraphQL query for the Storefront API looked like this: ` This was when I hit a roadblock. How do I fetch a field with a more complex type than a simple text or number, like an image? To retrieve the correct data, what specific details must I include in the originalLogo and themedLogo fields? In my quest for a solution, I turned to every resource I could think of. I combed through the Storefront API documentation, searched endlessly on Stack Overflow, and browsed various tech forums. Despite all these efforts, I couldn’t find the clear, detailed answers I needed. It felt like I was looking for something that should be there but wasn’t. Solution Before diving into the solution, it's important to note that this is the method I discovered through trial and error. There might be other approaches, but I want to share the process that worked for me without clear documentation. My first step was to understand the nature of the data returned by the Storefront API. I inspected the value of a metaobject, which looked something like this: ` The key here was the gid, or global unique identifier. What stood out was that it always includes the object type, in this case, MediaImage. This was crucial because it indicated which union to use and what properties to query from this object in the Storefront API documentation. So, I modified my query to include a reference to this object type, focusing on the originalLogo field as an example: ` The next step was to consult the Storefront API documentation for MediaImage at Shopify API Documentation. Here, I discovered the image field within MediaImage, an object containing the url field. With this information, I updated my query: ` Finally, when executing this query, the output for a single object was as follows: ` Through this process, I successfully extracted the necessary data from the object references in the metafields, specifically handling more complex data types like images. Conclusion In wrapping up, it's vital to emphasize that while this guide focused on extracting MediaImage data from Shopify's Storefront API, the methodology I've outlined is broadly applicable. The key is understanding the structure of the gid (global unique identifier) and using it to identify the correct object types within your GraphQL queries. Whether you're dealing with images or any other data type defined in Shopify's Storefront API, this approach can be your compass. Dive into the API documentation, identify the object types relevant to your needs, and adapt your queries accordingly. It's a versatile strategy that can be tailored to suit many requirements. Remember, the world of APIs and e-commerce is constantly evolving, and staying adaptable and resourceful is crucial. This journey has been a testament to the power of perseverance and creative problem-solving in the face of technical challenges. May your ventures into Shopify's Storefront API be equally rewarding and insightful....

Mar 29, 2024

4 mins

GraphQLShopify

How to Leverage Apollo Client Fetch Policies Like the Pros

Apollo Client provides a rich ecosystem and cache for interfacing with your GraphQL APIs. You write your query and leverage the useQuery hook to fetch your data. It provides you with some state context and eventually resolves your query. That data is stored in a local, normalized, in-memory cache, which allows Apollo Client to respond to most previously run requests near instantaneously. This has huge benefits for client performance and the feel of your apps. However, sometimes Apollo's default doesn't match the user experience you want to provide. They provide fetch policies to allow you to control this behavior on each query you execute. In this article, we'll explore the different fetch policies and how you should leverage them in your application. cache-first This is the default for Apollo Client. Apollo will execute your query against the cache. If the cache can fully fulfill the request, then that's it, and we return to the client. If it can only partially match your request or cannot find any of the related data, the query will be run against your GraphQL server. The response is cached for the next query and returned to the handler. This method prioritizes minimizing the number of requests sent to your server. However, it has an adverse effect on data that changes regularly. Think of your social media feeds - they typically contain constantly changing information as new posts are generated. Or a real-time dashboard app tracking data as it moves through a system. cache-first is probably not the best policy for these use cases as you won't fetch the latest data from the upstream source. You can lower the cache time of items for the dashboard to avoid the staleness issue and still minimize the requests being made, but this problem will persist for social media feeds. The cache-first policy should be considered for data that does not change often in your system or data that the current user fully controls. Data that doesn't change often is easily cached, and that's a recommended pattern. For data that the user controls, we need to consider how that data changes. If only the current user can change it, we have 2 options: Return the updated data in the response of any mutation which is used to update the cache Use cache invalidation methods like refetchQueries or onQueryUpdated These methods will ensure that our cache stays in sync with our server allowing the policy to work optimally. However, if other users in the system can make changes that impact the current user's view, then we can not invalidate the cache properly using these strategies which makes this policy unideal. network-only This policy skips the cache lookup and goes to the server to fetch the results. The results are stored in the cache for other operations to leverage. Going back to the example I gave in my explanation cache-first of a social media feed, the network-only policy would be a great way to implement the feed itself as it's ever-changing, and we'll likely even want to poll for changes every 10s or so. The following is an example of what this component could look like: ` Whenever this SocialFeed component is rendered, we always fetch the latest results from the GraphQL server ensuring we're looking at the current data. The results are put in the cache which we can leverage in some children components. cache-only cache-only only checks the cache for the requested data and never hits the server. It throws an error if the specified cache items cannot be found. At first glance, this cache policy may seem unhelpful because it's unclear if our cache is seeded with our data. However, in combination with the network-only policy above, this policy becomes helpful. This policy is meant for components down tree from network-only level query. This method is for you if you're a fan of React components' compatibility. We can modify the return of our previous example to be as follows: ` Notice we're not passing the full post object as a prop. This simplifies our Post component types and makes later refactors easier. The Post would like like the following: ` In this query, we're grabbing the data directly from our cache every time because our top-level query should have fetched it. Now, a small bug here makes maintainability a bit harder. Our top-level GetFeed query doesn't guarantee fetching the same fields. Notice how our Post component exports a fragment. Fragments are a feature Apollo supports to share query elements across operations. In our SocialFeed component, we can change our query to be: ` Now, as we change our Post to use new fields and display different data, the refactoring is restricted to just that component, and the upstream components will detect the changes and handle them for us making our codebase more maintainable. Because the upstream component is always fetching from the network, we can trust that the cache will have our data, making this component safe to render. With these examples, though, our users will likely have to see a loading spinner or state on every render unless we add some server rendering. cache-and-network This is where cache-and-network comes to play. With this policy, Apollo Client will run your query against your cache and your GraphQL server. This further simplifies our example above if we want to provide the last fetched results to the user but then update the feed immediately upon gathering the latest data. This is similar to what X/Twitter does when you reload the app. You'll see the last value that was in the cache then it'll render the network values when ready. This can cause a jarring user experience though, if the data is changing a lot over time, so I recommend using this methodology sparsely. However, if you wanted to update our existing example, we'd just change our SocialFeed component to use this policy, and that'll keep our client and server better in sync while still enabling 10s polling. no-cache This policy is very similar to the network-only policy, except it bypasses the local cache entirely. In our previous example, we wrote engagement as a sub-selector on a Post and stored fields there. These metrics can change in real time pretty drastically. Chat features, reactions, viewership numbers, etc., are all types of data that may change in real time. The no-cache policy is good when this type of data is active, such as during a live stream or within the first few hours of a post going out. You may typically want to use the cache-and-network policy eventually but during that active period, you'll probably want to use no-cache so your consumers can trust your data. I'd probably recommend changing your server to split these queries and run different policies for the operations for performance reasons. I haven't mentioned this yet, but you can make the fetch policy on a query dynamic, meaning you combine these different policies' pending states. This could look like the following: ` We pass whether the event is live to the component that then leverages that info to determine if we should cache or not when fetching the chat. That being said, we should consider using subscription operations for this type of feature as well, but that's an exercise for another blog post. standby This is the most uncommon fetch policy, but has a lot of use. This option runs like a cache-first query when it executes. However, by default, this query does not run and is treated like a "skip" until it is manually triggered by a refetch or updateQueries caller. You can achieve similar results by leveraging the useLazyQuery operator, but this maintains the same behavior as other useQuery operators so you'll have more consistency among your components. This method is primarily used for operations pending other queries to finish or when you want to trigger the caller on a mutation. Think about a dashboard with many filters that need to be applied before your query executes. The standby fetch policy can wait until the user hits the Apply or Submit button to execute the operation then calls a await client.refetchQueries({ include: ["DashboardQuery"] }), which will then allow your component to pull in the parameters for your operation and execute it. Again, you could achieve this with useLazyQuery so it's really up to you and your team how you want to approach this problem. To avoid learning 2 ways, though, I recommend picking just one path. Conclusion Apollo Client's fetch policies are a versatile and helpful tool for managing your application data and keeping it in sync with your GraphQL server. In general, you should use the defaults provided by the library, but think about the user experience you want to provide. This will help you determine which policy best meets your needs. Leveraging tools like fragments will enable you to manage your application and use composable patterns more effectively. With the rise of React Server Components and other similar patterns, you'll need to be wary of how that impacts your Apollo Client strategy. However, if you're on a legacy application that leverages traditional SSR patterns, Apollo allows you to pre-render queries on the server and their related cache. When you combine these technologies, you'll find that your apps perform great, and your users will be delighted....

May 17, 2024

7 mins

GraphQL

How to Resolve Nested Queries in Apollo Server

When working with relational data, there will be times when you will need to access information within nested queries. But how would this work within the context of Apollo Server? In this article, we will take a look at a few code examples that explore different solutions on how to resolve nested queries in Apollo Server. I have included all code examples in CodeSandbox if you are interested in trying them out on your own. Prerequisites This article assumes that you have a basic knowledge of GraphQL terminology. Table of Contents - How to resolve nested queries: An approach using resolvers and the filter method - A refactored approach using Data Loaders and Data Sources - What are Data Loaders - How to setup a Data Source - Setting up our schemas and resolvers - Resolving nested queries when microservices are involved - Conclusion How to resolve nested queries: An approach using resolvers and the filter method In this first example, we are going to be working with two data structures called musicBrands and musicAccessories. musicBrands is a collection of entities consisting of id and name. musicAccessories is a collection of entities consisting of the product name, price, id and an associated brandId. You can think of the brandId as a foreign key that connects the two database tables. We also need to set up the schemas for the brands and accessories. ` The next step is to set up a resolver for our Query to return all of the music accessories. ` When we run the following query and start the server, we will see this JSON output: ` ` As you can see, we are getting back the value of null for the brands field. This is because we haven't set up that relationship yet in the resolvers. Inside our resolver, we are going to create another query for the MusicAccessories and have the value for the brands key be a filtered array of results for each brand. ` When we run the query, this will be the final result: ` ` This single query makes it easy to access the data we need on the client side as compared to the REST API approach. If this were a REST API, then we would be dealing with multiple API calls and a Promise.all which could get a little messy. You can find the entire code in this CodeSandbox example. A refactored approach using Data Loaders and Data Sources Even though our first approach does solve the issue of resolving nested queries, we still have an issue fetching the same data repeatedly. Let’s look at this example query: ` If we take a look at the results, we are making additional queries for the brand each time we request the information. This leads to the N+1 problem in our current implementation. We can solve this issue by using Data Loaders and Data Sources. What are Data Loaders Data Loaders are used to batch and cache fetch requests. This allows us to fetch the same data and work with cached results, and reduce the number of API calls we have to make. To learn more about Data Loaders in GraphQL, please read this helpful article. How to setup a Data Source In this example, we will be using the following packages: - apollo-datasource - apollo-server-caching - dataloader We first need to create a BrandAccessoryDataSource class which will simulate the fetching of our data. ` We will then set up a constructor with a custom Dataloader. ` Right below our constructor, we will set up the context and cache. ` We then want to set up the error handling and cache keys for both the accessories and brands. To learn more about how caching works with GraphQL, please read through this article. ` Next, we are going to set up an asynchronous function called get which takes in an id. The goal of this function is to first check if there is anything in the cached results and if so return those cached results. Otherwise, we will set that data to the cache and return it. We will set the ttl(Time to Live in cache) value to 15 seconds. ` Below the get function, we will create another asynchronous function called getByBrand which takes in a brand. This function will have a similar setup to the get function but will filter out the data by brand. ` Setting up our schemas and resolvers The last part of this refactored example includes modifying the resolvers. We first need to add an accessory key to our Query schema. ` Inside the resolver, we will add the accessories key with a value for the function that returns the data source we created earlier. ` We also need to refactor our Brand resolver to include the data source we set up earlier. ` Lastly, we need to modify our ApolloServer object to include the BrandAccessoryDataSource. ` Here is the entire CodeSandbox example. When the server starts up, click on the Query your server button and run the following query: ` Resolving nested queries when microservices are involved Microservices is a type of architecture that will split up your software into smaller independent services. All of these smaller services can interact with a single API data layer. In this case, this data layer would be GraphQL. The client will interact directly with this data layer, and will consume API data from a single entry point. You would similarly resolve your nested queries as before because, at the end of the day, there are just functions. But now, this single API layer will reduce the number of requests made by the client because only the data layer will be called. This simplifies the data fetching experience on the client side. Conclusion In this article, we looked at a few code examples that explored different solutions on how to resolve nested queries in Apollo Server. The first approach involved creating custom resolvers and then using the filter method to filter out music accessories by brand. We then refactored that example to use a custom DataLoader and Data Source to fix the "N+1 problem". Lastly, we briefly touched on how to approach this solution if microservices were involved. If you want to get started with Apollo Server and build your own nested queries and resolvers using these patterns, check out our serverless-apollo-contentful starter kit!...

May 17, 2023

6 mins

GraphQL

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

Let's innovate together!

We're ready to be your trusted technical partners in your digital innovation journey.

Whether it's modernization or custom software solutions, our team of experts can guide you through best practices and how to build scalable, performant software that lasts.

Introducing the New Serverless, GraphQL, Apollo Server, and Contentful Starter kit

Introducing the new Serverless, GraphQL, Apollo Server, and Contentful Starter kit

Table of Contents

How to get started setting up the kit

Generate the project

Setup Contentful access

Setting up Docker

Starting the local server

How to Create the Technology Model in Contentful

How to seed the database with demo data

How to work with the migration scripts

Technologies included in this starter kit

Why use GraphQL?

Why use Contentful?

Why use Amazon Simple Queue Service (SQS)?

Why use Apollo Server?

Why use the Serverless Framework?

Why use Redis?

Why use the Jest testing framework?

Project structure

How to deploy your application

What can this starter kit be used for?

Conclusion

Jessica Wilkins

You might also like

Efficiently Extract Object References in Shopify Storefront GraphQL API

How to Leverage Apollo Client Fetch Policies Like the Pros

How to Resolve Nested Queries in Apollo Server

Increasing development velocity with Cursor

Let's innovate together!

You might also like

Efficiently Extract Object References in Shopify Storefront GraphQL API

How to Leverage Apollo Client Fetch Policies Like the Pros

How to Resolve Nested Queries in Apollo Server

Increasing development velocity with Cursor