GraphQL Best Practices with AI Tools – Training Recap at S3Corp.

On Friday, 29 September 2025, S3Corp. organized an internal knowledge-sharing session titled GraphQL Best Practices with AI Tools. This session focus on four areas: an introduction to GraphQL, optimization of field arguments, and the N+1 problem with the Data Loader. Each of them focused on application-oriented problem-solving with additional advice on the usage of AI for automating work.

Introduction to GraphQL. 

The session started with defining what GraphQL is, and understanding the reason for its popularity.

GraphQL is a querying language and runtime system for requesting specific data, rather than a large, predefined payload. Instead of several REST endpoints, one GraphQL endpoint can be used to respond to different queries.

Let’s illustrate this by comparing a REST API implementation to a GraphQL query.

• With REST, a client who wants both user profile data and a user’s posts would manually combine responses from two different endpoints.
• With GraphQL, a client would only need to issue a single query to retrieve both datasets in the precise arrangement desired. This consolidation of requests eliminates the need to reduce network requests and resolves the over-fetching or under-fetching cases. 

The introduction also covered the value of a strongly typed schema. GraphQL schemas outline what data is, how it is structured, and what operations can be performed on it. For instance, a ‘user’ type might have the fields ‘id’, ‘name’ and ‘email’. As type safety is enforced in the schema, developers and clients alike are safe to assume that the results yielded are stable and consistent. Schema validation also caps errors that can occur during query time and thus enhances the value and reliability of integrations with other systems. 

The example served to illustrate how GraphQL facilitates the execution of nested queries. A client is able to request not only user data but also associated posts, comments, or other related entities in a single operation. This capability is particularly beneficial for applications with complex relationships, as it reduces the complexity of managing multiple requests.

AI tools began to be utilized as intelligent assistants at this stage. They have the capability to analyze query usage patterns, propose schema enhancements, and even assist in the identification of redundant or absent types. This level of assistance is extremely beneficial to teams who work on the management of large-scale APIs and can help ease the burden of the maintenance and documentation of schema.

Optimizing Field Arguments

The second focus of the training dealt with the application of field arguments in GraphQL queries. Arguments are elements of the query that define the scope of data to be retrieved, filtered or shaped. When properly applied, arguments can eliminate some performance bottlenecks and return targeted data to the user with the desired level of precision.

For instance, an example query can be one that is designed to return a list of articles. In the absence of arguments, the query would be designed to return all articles, regardless of whether the user wanted only the top 10 most recent articles. However, with the arguments limit and offset, the developers are able to set pagination and keep the response light.

The facilitator showed some patterns of different arguments. Sorting arguments can enable results to be ordered in either ascending or descending order. Filtering arguments are able to set results to only specific category or even specific status. Date range arguments are able to limit the results of the query to only a specific time range. With the use of these practices, the load on the server is minimized, as more computation is done at query time rather than data post-processed on the client side.

The training also identified the risks associated with poorly designed arguments. For example, unrestricted queries could deteriorate performance because clients could unwittingly ask for huge data sets. Hence, best practices suggest stopping such behavior with setting limits, value validation, argument value defaults, and argument overrides for predictable behavior.

Participants considered how AI could help defend and optimize arguments. AI could go through query logs and flag frequently used filters or unused arguments. With that understanding, AI could suggest improving the schema by adding missing arguments or removing redundant ones. AI could also recognize unproductive query patterns, such as pulling large unpaginated data sets numerous times, and recommend tighter controls.  

One case study involved an application that lists and displays the products in a catalog. With no arguments, users searching for items by category or within a particular price range need to use client-side filters, incurring unnecessary data transfer. With the introduction of arguments such as category, priceMin, and priceMax, the server filters and returns relevant results. AI in this case would optimize queries and inform developers if certain filters induce excessive load to the database.

Solving the N+1 Problem with Data Loader

The last issue that we covered was the N+1 problem which is a typical problem in the performance of GraphQl applications where there are many repetitive database calls while sorting out a specific query. 

To illustrate the problem, imagine a query that fetches all the users and the orders associated with them. The unoptimized server will first fetch all the users and then create a new order query for each user. When there are 100 users, the server will perform 101 queries in total which is an inefficient approach and highly burdens the database. 

The training demonstrated the N+1 problem and its solution, which is provided by the Data Loader. Data Loader combines requests and caches responses to cut down query volume. In this case, Data Loader saved multiple queries to users by bundling them and completing them with a single order query to the database. Thus, there was a significant improvement in efficiency and latency. 

The developers were then guided on how to implement. In this case, Data Loader functions to accept and return complete user ID batches with one response. The GraphQL resolver will not execute a direct database query and rather pass the request to Data Loader which will return the cached data or queue the request for batch processing.

In the meeting, the appropriate procedures for utilizing Data Loader was showcased, and the necessity for minimizing the method's instantiations was underscored as avoiding user data leaks was pivotal. Caching strategies need to be tuned as placing too much focus on memory usage can be at the expense of performance improvements. Data Loader's logic should be integrated with the existing resolvers to keep the code clean and to avoid loss of predictability.

Here too, the focus of AI features is ancillary. They can assess the execution of queries and ascertain N+1 problems and the places where Data Loader is applicable. For instance, AI might conclude that, within a particular Data Loader, there is a resolver that continuously performs a few hundred database queries that are redundant and should be prioritized for optimization.

Along with the theoretical aspects, a specific use case was presented where Data Loader decreased the time to execute a particular query. Batching requests for related data enabled the application to return results faster, despite the increasing user load. This enhancement was the result of a proactive approach to solving the N+1 problem, and it ascertained the importance of such approaches.

Training Culture at S3Corp. 

The session on GraphQL best practices is not simply focused on something technical. It is also a snapshot of a culture of continuouslearning within S3Corp.. Each training session augments team proficiency and collaboration. The focus on practical situational learning ensured that the knowledge acquired was applicable to the practical problems of the projects. 

Participants appreciated the discussion on the applicationof AI tools. It illustrated the ways in which modern developments integrate more and more human effort into intelligent automation. Learning AI applications in query optimization, argument structuring, and performance evaluation plans to better prepare the team toward the future of softwaredevelopment.