You need customer data, their recent orders, and their support tickets. Your REST API gives you three endpoints.
Three requests. Three round trips. The mobile app feels sluggish.
Or worse: you get the customer data and it includes 47 fields you don't need.
You should be able to ask for exactly what you need, once.
FOUNDATIONAL - GraphQL provides the query layer that AI systems use to fetch precisely the context they need.
GraphQL is a query language for your API. Instead of hitting fixed endpoints that return fixed data, clients write queries describing exactly what they want. Want the customer name and their last three orders? Ask for that. Want their support tickets instead? Change the query.
The server exposes a schema, basically a contract of what data exists and how it connects. Clients explore that schema and build queries. The server resolves those queries and returns exactly what was asked. No more, no less.
When AI systems need context, they rarely need 'everything about this customer.' GraphQL lets them request precisely the fields that matter for the current task.
GraphQL solves a universal problem: how do you give clients flexibility without building a thousand endpoints or returning data they don't need?
Define a schema of types and relationships. Let clients query that schema declaratively. Resolve queries by fetching only what was requested. This pattern scales from a single data source to complex federated systems.
Toggle the fields below. Watch how GraphQL adapts while REST returns everything regardless.
Returns all 31 fields, whether you need them or not.
query {
customer(id: 1) {
name
email
orders {
id
product
amount
status
date
}
}
}Returns exactly 17 fields. 45% less data.
The contract between client and server
You define types (Customer, Order, Product) and their fields. You define how they connect (Customer has many Orders). The schema is the source of truth. Clients can introspect it to see what's available.
Clients describe what they want
Clients write queries that mirror the shape of the data they want back. Nested queries follow relationships naturally. Want customer name and their orders? The query nests orders inside customer.
The server fetches what was requested
Each field in the schema has a resolver function. When a query asks for that field, the resolver runs. Resolvers can hit databases, call other APIs, compute values. They only run for requested fields.
Your AI assistant needs context: customer profile, recent orders, open tickets. With REST, that's three round trips and data you don't need. GraphQL lets the assistant ask for exactly what matters, in one query that returns in 200ms.
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You generate a GraphQL schema from your database tables because it's fast. Now your internal column names like "cust_addr_line_1" are in your API. Clients depend on them. Renaming anything breaks everything.
Instead: Design your GraphQL schema for clients. Map it to your database in resolvers.
A client writes a query that nests 10 levels deep and requests every field. Your server tries to resolve it. The database melts. The page times out. You get paged at 2am.
Instead: Add query complexity limits. Set maximum depth. Use persisted queries for untrusted clients.
You fetch a list of 50 customers, then for each customer, you fetch their orders in a separate query. That's 51 database queries for one GraphQL request. It works in dev. Production with 1000 customers? Toast.
Instead: Use DataLoader or similar batching. Fetch orders for all customers in one query.
You've learned how schemas, queries, and resolvers work together. The natural next step is understanding how data from GraphQL endpoints gets transformed and mapped for downstream systems.